MA thesis
Michael Carnovale
1 Load required packages
library(caret) ## Package to help with machine learning functions
library(psych) ## Descriptive stats and reliability statistics
library(dplyr) ## Data management
library(car) ## More data management
library(foreign) ## To import datasets
library(effsize) ## Effect sizes with CIs
library(visreg) ## Visualization of regression models
library(jtools) ## Various functions for regression models
library(glmnet) ## Penalized regression package
library(summarytools)
library(boot) ## Bootstrapping## boot summarytools glmnet foreach Matrix
## "1.3-20" "0.9.3" "2.0-16" "1.4.4" "1.2-15"
## jtools visreg effsize foreign car
## "2.0.0" "2.5-0" "0.7.4" "0.8-71" "3.0-2"
## carData dplyr psych caret ggplot2
## "3.0-2" "0.8.3" "1.8.12" "6.0-81" "3.2.1"
## lattice
## "0.20-38"2 Data management
# Subset variables (FFM facets, time 1 and 2 HAMD/BDI-II sum scores, treatment group, age, sex)
neo.depr.1 = neo.depr.1[,c(252:281, 642, 647, 652, 653, 1, 7, 4, 5)]
neo.depr.2 = neo.depr.2[,c(9:38, 84, 90, 79, 82, 98, 7, 8)]
names(neo.depr.1)## [1] "NRN1.1" "NRN2.1" "NRN3.1" "NRN4.1" "NRN5.1" "NRN6.1" "NRE1.1"
## [8] "NRE2.1" "NRE3.1" "NRE4.1" "NRE5.1" "NRE6.1" "NRO1.1" "NRO2.1"
## [15] "NRO3.1" "NRO4.1" "NRO5.1" "NRO6.1" "NRA1.1" "NRA2.1" "NRA3.1"
## [22] "NRA4.1" "NRA5.1" "NRA6.1" "NRC1.1" "NRC2.1" "NRC3.1" "NRC4.1"
## [29] "NRC5.1" "NRC6.1" "HD_17.1" "HD_17.2" "BDI2.1" "BDI2.2" "ccode"
## [36] "txgrp" "P121" "age"names(neo.depr.2)## [1] "NRN1.00" "NRN2.00" "NRN3.00" "NRN4.00" "NRN5.00" "NRN6.00"
## [7] "NRE1.00" "NRE2.00" "NRE3.00" "NRE4.00" "NRE5.00" "NRE6.00"
## [13] "NRO1.00" "NRO2.00" "NRO3.00" "NRO4.00" "NRO5.00" "NRO6.00"
## [19] "NRA1.00" "NRA2.00" "NRA3.00" "NRA4.00" "NRA5.00" "NRA6.00"
## [25] "NRC1.00" "NRC2.00" "NRC3.00" "NRC4.00" "NRC5.00" "NRC6.00"
## [31] "HD_17.00" "HD_17.16" "bdi2.00" "bdi2.16" "txgrp" "sex"
## [37] "age"# Cut down to participants in only the first three studies in the first dataset
neo.depr.1 = subset(neo.depr.1, ccode == "UPC" | ccode == "NPL" | ccode == "OMHF")
# Renaming variables in second dataset to match the variable names in first dataset
depr.1.names = colnames(neo.depr.1[c(1:34, 36:38)])
names(neo.depr.2) = depr.1.names
# Adding in study code for second dataset
neo.depr.2$ccode = "Quilty et al."
neo.depr.2$ccode = as.factor(neo.depr.2$ccode)
# Rescoring FFM facets in Quilty et al.
ffm.names = names(neo.depr.2[,c(1:30)])
better.ffm = neo.depr.2[,c(ffm.names)] - 4
neo.depr.2 = neo.depr.2[,c(31:38)]
neo.depr.2 = cbind(better.ffm, neo.depr.2)
# Combining both datasets into one for descriptive statistics
neo.depr.full = rbind(neo.depr.1, neo.depr.2)
neo.depr.full$txgrp = droplevels(neo.depr.full$txgrp)
neo.depr.full$ccode = droplevels(neo.depr.full$ccode)
neo.depr.1$txgrp = droplevels(neo.depr.1$txgrp)
neo.depr.2$txgrp = droplevels(neo.depr.2$txgrp)
neo.depr.1$ccode = droplevels(neo.depr.1$ccode)
neo.depr.2$ccode = droplevels(neo.depr.2$ccode)
# Taking out participants who did ipt
table(neo.depr.full$txgrp) # 45 participants##
## ipt cbt med
## 45 94 265neo.depr.full = subset(neo.depr.full, txgrp == "cbt" | txgrp == "med")
neo.depr.full$txgrp = droplevels(neo.depr.full$txgrp)
# Recoding sex variable
neo.depr.full$P121 = neo.depr.full$P121 - 1
neo.depr.full$P121 = as.factor(neo.depr.full$P121)
# Recoding treatment variable
neo.depr.full$txgrp = as.numeric(neo.depr.full$txgrp)
neo.depr.full$txgrp = neo.depr.full$txgrp - 1 # 0 = cbt, 1 = meds
# Renaming variables in full dataset
neo.depr.full = rename(neo.depr.full, sex = P121, hamd1 = HD_17.1, hamd2 = HD_17.2)2.1 Making separate datasets
neo.depr.cbt = subset(neo.depr.full, txgrp==0) # CBT participants only
neo.depr.med = subset(neo.depr.full, txgrp==1) # Med participants only
neo.depr.cbt.bdi = neo.depr.cbt[, c(1:30, 33:38)] # CBT participants with BDI-II data
neo.depr.med.bdi = neo.depr.med[, c(1:30, 33:38)] # Med participants with BDI-II data
neo.depr.cbt.hd = neo.depr.cbt[, c(1:32, 35:38)] # CBT participants with HAM-D data
neo.depr.med.hd = neo.depr.med[, c(1:32, 35:38)] # Med participants with HAM-D data
neo.depr.bdi = neo.depr.full[,c(1:30, 33:38)] # Participants with BDI-II data, across txgrp
neo.depr.hd = neo.depr.full[,c(1:32, 35:38)] # Participants with HAM-D data, across txgrp2.2 Handling missing data
# Checking for NAs
colSums(is.na(neo.depr.full)) # Number of NAs per variable in full data## NRN1.1 NRN2.1 NRN3.1 NRN4.1 NRN5.1 NRN6.1 NRE1.1 NRE2.1 NRE3.1 NRE4.1
## 0 0 0 0 0 0 0 0 0 0
## NRE5.1 NRE6.1 NRO1.1 NRO2.1 NRO3.1 NRO4.1 NRO5.1 NRO6.1 NRA1.1 NRA2.1
## 0 0 0 0 0 0 0 0 0 0
## NRA3.1 NRA4.1 NRA5.1 NRA6.1 NRC1.1 NRC2.1 NRC3.1 NRC4.1 NRC5.1 NRC6.1
## 0 0 0 0 0 0 0 0 0 0
## hamd1 hamd2 BDI2.1 BDI2.2 ccode txgrp sex age
## 6 28 133 151 0 0 3 3describeBy(neo.depr.full$BDI2.1, group = neo.depr.full$ccode) # First study didn't have any BDI-II data from participants## Warning in min(x, na.rm = na.rm): no non-missing arguments to min;
## returning Inf## Warning in max(x, na.rm = na.rm): no non-missing arguments to max;
## returning -Inf##
## Descriptive statistics by group
## group: UPC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 0 NaN NA NA NaN NA Inf -Inf -Inf NA NA NA
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 45 31.45 10.87 30 30.82 8.9 13 60 47 0.57 0.02 1.62
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 77 30.09 7.46 29.97 29.56 7.37 17 52 35 0.72 0.34 0.85
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 104 29.92 8.59 30.5 29.92 8.15 8 49 41 -0.06 -0.32
## se
## X1 0.84# Listwise deletion first
neo.depr.cbt.bdi = na.omit(neo.depr.cbt.bdi)
neo.depr.cbt.hd = na.omit(neo.depr.cbt.hd)
neo.depr.med.bdi = na.omit(neo.depr.med.bdi)
neo.depr.med.hd = na.omit(neo.depr.med.hd)
neo.depr.bdi = na.omit(neo.depr.bdi)
neo.depr.hd = na.omit(neo.depr.hd)3 Descriptive stats
3.1 Full sample before listwise deletion
## Sample size
table(neo.depr.full$ccode)##
## UPC NPL OMHF Quilty et al.
## 133 45 77 104## Tx group, and tx by study
table(neo.depr.full$txgrp)##
## 0 1
## 94 265table(neo.depr.full$ccode, neo.depr.full$txgrp) ##
## 0 1
## UPC 0 133
## NPL 0 45
## OMHF 40 37
## Quilty et al. 54 503.2 Participants with full BDI-II data
## Sample size
nrow(neo.depr.bdi)## [1] 208## Study
table(neo.depr.bdi$ccode)##
## UPC NPL OMHF Quilty et al.
## 0 45 77 86## Tx group
table(neo.depr.bdi$txgrp)##
## 0 1
## 88 120table(neo.depr.bdi$ccode, neo.depr.bdi$txgrp)##
## 0 1
## UPC 0 0
## NPL 0 45
## OMHF 40 37
## Quilty et al. 48 38## Sex
freq(neo.depr.bdi$sex)## Frequencies
## neo.depr.bdi$sex
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 0 83 39.90 39.90 39.90 39.90
## 1 125 60.10 100.00 60.10 100.00
## <NA> 0 0.00 100.00
## Total 208 100.00 100.00 100.00 100.00## Age
describe(neo.depr.bdi$age, quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 37.31 11.42 36 36.77 13.34 18 65 47 0.37 -0.97
## se Q0.25 Q0.75
## 1 0.79 27.75 46.25hist(neo.depr.bdi$age)
densityPlot(neo.depr.bdi$age)
describeBy(neo.depr.bdi$age, group = neo.depr.bdi$ccode, quant = c(.25, .75)) # By study##
## Descriptive statistics by group
## group: UPC
## NULL
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 45 37.11 11.67 34 36.73 14.83 20 62 42 0.33 -1.18
## se Q0.25 Q0.75
## 1 1.74 28 48
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 77 41.51 11.74 40 41.46 14.83 21 65 44 0.02 -1.1
## se Q0.25 Q0.75
## 1 1.34 31 52
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 86 33.65 9.72 30 33.01 8.9 18 59 41 0.59 -0.71 1.05
## Q0.25 Q0.75
## 1 26 42## FFM facets
describe(neo.depr.bdi[,c(1:30)], quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew
## NRN1.1 1 208 19.74 5.32 20.0 19.81 5.93 4 32 28 -0.14
## NRN2.1 2 208 16.52 5.26 16.0 16.29 5.93 5 30 25 0.32
## NRN3.1 3 208 24.08 4.35 24.5 24.24 3.71 9 32 23 -0.46
## NRN4.1 4 208 19.79 5.26 20.0 19.93 5.93 6 31 25 -0.25
## NRN5.1 5 208 17.58 5.32 18.0 17.57 5.93 3 32 29 0.04
## NRN6.1 6 208 15.70 4.84 16.0 15.65 4.45 3 27 24 0.06
## NRE1.1 7 208 18.15 5.38 18.0 18.22 5.93 5 32 27 -0.12
## NRE2.1 8 208 12.24 5.88 13.0 12.18 5.93 0 30 30 0.13
## NRE3.1 9 208 11.43 5.46 11.0 11.29 5.93 0 24 24 0.22
## NRE4.1 10 208 13.16 4.76 13.0 12.95 4.45 1 31 30 0.42
## NRE5.1 11 208 16.07 4.97 17.0 16.17 4.45 2 30 28 -0.17
## NRE6.1 12 208 11.75 5.72 12.0 11.62 5.93 0 26 26 0.16
## NRO1.1 13 208 17.30 5.63 17.0 17.32 5.93 0 32 32 -0.07
## NRO2.1 14 208 18.57 6.03 19.0 18.75 5.93 3 31 28 -0.32
## NRO3.1 15 208 20.47 4.73 21.0 20.57 4.45 8 31 23 -0.20
## NRO4.1 16 208 13.57 4.88 14.0 13.53 4.45 1 26 25 0.04
## NRO5.1 17 208 19.90 5.76 20.0 20.02 5.93 7 31 24 -0.21
## NRO6.1 18 208 20.15 4.65 20.5 20.33 5.19 5 30 25 -0.35
## NRA1.1 19 208 15.88 5.30 16.0 15.89 5.93 2 31 29 0.00
## NRA2.1 20 208 17.60 5.35 17.0 17.73 5.93 1 29 28 -0.27
## NRA3.1 21 208 21.04 4.14 21.0 21.01 4.45 10 32 22 0.10
## NRA4.1 22 208 14.96 5.14 15.0 14.90 4.45 3 30 27 0.12
## NRA5.1 23 208 19.23 4.89 20.0 19.39 4.45 6 29 23 -0.29
## NRA6.1 24 208 21.26 3.77 21.0 21.30 4.45 12 31 19 -0.01
## NRC1.1 25 208 16.87 4.90 17.0 16.94 4.45 6 31 25 -0.07
## NRC2.1 26 208 13.52 5.94 13.0 13.63 7.41 0 27 27 -0.15
## NRC3.1 27 208 19.33 4.54 20.0 19.27 4.45 9 32 23 0.14
## NRC4.1 28 208 13.74 5.70 14.0 13.65 5.93 1 31 30 0.19
## NRC5.1 29 208 11.43 5.93 11.0 11.26 5.93 0 25 25 0.23
## NRC6.1 30 208 15.69 5.42 16.0 15.64 4.45 3 30 27 0.09
## kurtosis se Q0.25 Q0.75
## NRN1.1 -0.06 0.37 16.00 23.00
## NRN2.1 -0.36 0.37 12.75 20.00
## NRN3.1 0.16 0.30 21.75 27.00
## NRN4.1 -0.32 0.36 16.00 24.00
## NRN5.1 -0.18 0.37 14.00 21.00
## NRN6.1 -0.27 0.34 12.75 19.00
## NRE1.1 -0.35 0.37 14.00 22.00
## NRE2.1 -0.10 0.41 8.00 16.00
## NRE3.1 -0.56 0.38 8.00 15.00
## NRE4.1 0.38 0.33 10.00 16.00
## NRE5.1 0.16 0.34 13.00 19.00
## NRE6.1 -0.62 0.40 7.00 16.00
## NRO1.1 -0.04 0.39 13.00 21.00
## NRO2.1 -0.47 0.42 15.00 22.25
## NRO3.1 -0.30 0.33 17.00 23.25
## NRO4.1 -0.36 0.34 10.00 17.00
## NRO5.1 -0.69 0.40 16.00 24.00
## NRO6.1 -0.18 0.32 17.00 24.00
## NRA1.1 -0.24 0.37 12.00 20.00
## NRA2.1 -0.26 0.37 14.00 22.00
## NRA3.1 -0.44 0.29 18.00 24.00
## NRA4.1 -0.34 0.36 12.00 18.25
## NRA5.1 -0.42 0.34 16.00 23.00
## NRA6.1 -0.23 0.26 19.00 24.00
## NRC1.1 -0.32 0.34 13.00 20.00
## NRC2.1 -0.73 0.41 9.00 18.00
## NRC3.1 -0.13 0.31 16.00 22.00
## NRC4.1 -0.17 0.40 9.75 17.00
## NRC5.1 -0.77 0.41 7.00 16.00
## NRC6.1 -0.27 0.38 13.00 19.00describeBy(neo.depr.bdi[,c(1:30)], group = neo.depr.bdi$ccode, quant = c(.25, .75)) # By study##
## Descriptive statistics by group
## group: UPC
## NULL
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis
## NRN1.1 1 45 21.58 4.90 22 21.49 5.93 13 31 18 0.10 -1.04
## NRN2.1 2 45 17.91 5.69 18 17.86 5.93 5 30 25 -0.05 -0.62
## NRN3.1 3 45 23.73 5.06 25 24.11 4.45 9 32 23 -0.78 0.13
## NRN4.1 4 45 19.51 5.51 21 19.78 4.45 6 29 23 -0.47 -0.41
## NRN5.1 5 45 18.07 5.84 17 17.89 5.93 6 31 25 0.30 -0.42
## NRN6.1 6 45 18.11 4.75 18 18.03 4.45 10 27 17 0.24 -0.87
## NRE1.1 7 45 17.89 6.19 18 17.92 7.41 5 30 25 -0.02 -0.85
## NRE2.1 8 45 12.58 5.60 13 12.51 4.45 0 25 25 0.07 -0.22
## NRE3.1 9 45 12.89 5.01 13 12.92 5.93 3 24 21 0.05 -0.75
## NRE4.1 10 45 13.73 5.31 13 13.30 4.45 4 31 27 0.85 1.11
## NRE5.1 11 45 14.76 4.87 15 14.89 4.45 5 23 18 -0.24 -0.75
## NRE6.1 12 45 12.76 6.30 12 12.62 5.93 0 26 26 0.23 -0.83
## NRO1.1 13 45 19.49 5.51 19 19.57 5.93 8 30 22 0.00 -0.85
## NRO2.1 14 45 19.64 5.98 20 19.76 5.93 7 31 24 -0.11 -0.64
## NRO3.1 15 45 20.80 5.55 22 21.00 4.45 8 31 23 -0.39 -0.31
## NRO4.1 16 45 14.69 4.25 14 14.38 2.97 5 26 21 0.68 0.58
## NRO5.1 17 45 20.33 6.83 21 20.46 8.90 8 31 23 -0.18 -1.24
## NRO6.1 18 45 21.87 4.20 22 21.97 4.45 14 29 15 -0.22 -0.98
## NRA1.1 19 45 17.27 5.87 18 17.38 5.93 3 31 28 -0.19 -0.31
## NRA2.1 20 45 20.27 3.39 20 20.24 4.45 14 28 14 0.14 -0.89
## NRA3.1 21 45 22.87 4.35 24 22.97 4.45 13 32 19 -0.23 -0.64
## NRA4.1 22 45 17.40 4.42 17 17.35 4.45 5 26 21 -0.05 0.07
## NRA5.1 23 45 21.40 4.33 22 21.65 4.45 8 29 21 -0.71 0.46
## NRA6.1 24 45 22.44 3.77 23 22.54 2.97 12 31 19 -0.26 0.40
## NRC1.1 25 45 18.91 5.64 19 19.14 4.45 6 31 25 -0.41 -0.01
## NRC2.1 26 45 16.84 4.78 18 17.05 2.97 5 26 21 -0.48 -0.15
## NRC3.1 27 45 19.18 4.67 20 19.24 5.93 9 29 20 -0.16 -0.80
## NRC4.1 28 45 14.76 6.96 15 14.57 7.41 3 31 28 0.13 -0.73
## NRC5.1 29 45 13.80 5.62 15 13.92 5.93 1 24 23 -0.25 -0.97
## NRC6.1 30 45 17.58 5.75 18 17.65 4.45 5 30 25 -0.06 -0.21
## se Q0.25 Q0.75
## NRN1.1 0.73 17 24
## NRN2.1 0.85 14 22
## NRN3.1 0.75 20 27
## NRN4.1 0.82 16 23
## NRN5.1 0.87 14 21
## NRN6.1 0.71 15 21
## NRE1.1 0.92 13 23
## NRE2.1 0.83 9 16
## NRE3.1 0.75 9 16
## NRE4.1 0.79 10 16
## NRE5.1 0.73 12 18
## NRE6.1 0.94 9 17
## NRO1.1 0.82 15 23
## NRO2.1 0.89 16 23
## NRO3.1 0.83 17 24
## NRO4.1 0.63 12 17
## NRO5.1 1.02 15 26
## NRO6.1 0.63 19 25
## NRA1.1 0.87 13 21
## NRA2.1 0.51 18 23
## NRA3.1 0.65 20 26
## NRA4.1 0.66 14 20
## NRA5.1 0.65 18 24
## NRA6.1 0.56 21 25
## NRC1.1 0.84 16 22
## NRC2.1 0.71 14 20
## NRC3.1 0.70 16 23
## NRC4.1 1.04 10 20
## NRC5.1 0.84 9 18
## NRC6.1 0.86 15 20
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis
## NRN1.1 1 77 20.94 5.43 20 20.98 5.93 7 32 25 -0.08 -0.45
## NRN2.1 2 77 17.05 5.33 17 16.78 5.93 6 29 23 0.34 -0.69
## NRN3.1 3 77 23.99 4.08 24 24.00 4.45 14 32 18 -0.05 -0.53
## NRN4.1 4 77 19.48 5.25 19 19.46 4.45 8 31 23 0.06 -0.39
## NRN5.1 5 77 18.74 5.18 19 18.73 5.93 8 32 24 0.05 -0.39
## NRN6.1 6 77 16.73 4.54 17 16.73 4.45 6 27 21 -0.01 -0.70
## NRE1.1 7 77 17.58 5.37 17 17.59 5.93 6 30 24 -0.04 -0.54
## NRE2.1 8 77 14.01 5.91 14 14.11 5.93 1 30 29 -0.04 0.17
## NRE3.1 9 77 12.82 5.41 13 12.67 5.93 4 24 20 0.22 -0.90
## NRE4.1 10 77 14.29 4.60 14 14.13 4.45 6 24 18 0.27 -0.84
## NRE5.1 11 77 15.35 4.91 16 15.37 4.45 3 27 24 -0.11 -0.29
## NRE6.1 12 77 13.05 5.61 13 13.05 5.93 1 25 24 -0.03 -0.73
## NRO1.1 13 77 18.45 5.36 19 18.35 5.93 6 32 26 0.07 -0.36
## NRO2.1 14 77 18.03 6.21 19 18.37 5.93 3 29 26 -0.56 -0.49
## NRO3.1 15 77 20.84 4.44 22 21.03 4.45 9 29 20 -0.41 -0.21
## NRO4.1 16 77 15.92 4.28 16 16.10 4.45 6 25 19 -0.28 -0.65
## NRO5.1 17 77 19.25 5.71 20 19.37 5.93 7 30 23 -0.28 -0.66
## NRO6.1 18 77 22.36 3.60 23 22.44 2.97 12 30 18 -0.31 0.06
## NRA1.1 19 77 17.01 5.51 17 17.19 5.93 2 29 27 -0.27 -0.18
## NRA2.1 20 77 20.18 4.42 21 20.24 5.93 11 29 18 -0.10 -0.84
## NRA3.1 21 77 21.58 3.69 22 21.59 4.45 13 31 18 0.08 -0.41
## NRA4.1 22 77 17.01 4.86 17 17.02 4.45 6 30 24 0.08 -0.54
## NRA5.1 23 77 20.55 3.76 20 20.43 4.45 12 29 17 0.25 -0.25
## NRA6.1 24 77 20.27 3.65 20 20.33 2.97 13 28 15 -0.07 -0.67
## NRC1.1 25 77 17.74 4.37 18 17.95 4.45 7 25 18 -0.45 -0.24
## NRC2.1 26 77 16.17 4.89 17 16.30 5.93 4 27 23 -0.25 -0.56
## NRC3.1 27 77 20.08 3.96 20 20.05 4.45 11 30 19 0.12 -0.43
## NRC4.1 28 77 14.94 4.67 15 14.97 4.45 4 29 25 0.04 0.31
## NRC5.1 29 77 13.73 5.59 13 13.62 5.93 3 25 22 0.18 -0.83
## NRC6.1 30 77 16.19 4.82 16 16.22 4.45 3 28 25 -0.05 -0.28
## se Q0.25 Q0.75
## NRN1.1 0.62 18 25
## NRN2.1 0.61 13 21
## NRN3.1 0.46 21 27
## NRN4.1 0.60 16 23
## NRN5.1 0.59 16 23
## NRN6.1 0.52 13 20
## NRE1.1 0.61 14 22
## NRE2.1 0.67 11 18
## NRE3.1 0.62 8 17
## NRE4.1 0.52 11 18
## NRE5.1 0.56 11 18
## NRE6.1 0.64 8 17
## NRO1.1 0.61 14 22
## NRO2.1 0.71 15 22
## NRO3.1 0.51 18 24
## NRO4.1 0.49 13 19
## NRO5.1 0.65 15 23
## NRO6.1 0.41 21 24
## NRA1.1 0.63 14 21
## NRA2.1 0.50 17 23
## NRA3.1 0.42 19 24
## NRA4.1 0.55 14 21
## NRA5.1 0.43 18 23
## NRA6.1 0.42 18 23
## NRC1.1 0.50 15 21
## NRC2.1 0.56 13 20
## NRC3.1 0.45 17 22
## NRC4.1 0.53 12 18
## NRC5.1 0.64 10 18
## NRC6.1 0.55 13 20
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis
## NRN1.1 1 86 17.71 4.80 18.0 18.06 4.45 4 27 23 -0.61 -0.08
## NRN2.1 2 86 15.31 4.75 15.0 15.17 4.45 5 30 25 0.40 0.19
## NRN3.1 3 86 24.35 4.22 25.0 24.50 4.45 12 32 20 -0.43 0.13
## NRN4.1 4 86 20.22 5.18 21.0 20.41 5.93 6 30 24 -0.38 -0.29
## NRN5.1 5 86 16.29 4.94 17.0 16.39 4.45 3 25 22 -0.31 -0.49
## NRN6.1 6 86 13.52 4.26 14.0 13.60 4.45 3 23 20 -0.21 -0.42
## NRE1.1 7 86 18.80 4.90 19.0 18.93 4.45 6 32 26 -0.22 0.12
## NRE2.1 8 86 10.47 5.54 11.0 10.36 5.93 0 27 27 0.25 -0.19
## NRE3.1 9 86 9.42 5.14 9.0 9.24 4.45 0 22 22 0.35 -0.21
## NRE4.1 10 86 11.85 4.32 12.0 11.84 4.45 1 24 23 0.05 -0.08
## NRE5.1 11 86 17.41 4.81 17.5 17.46 3.71 2 30 28 -0.19 1.01
## NRE6.1 12 86 10.05 5.11 10.0 10.04 5.93 0 22 22 0.09 -0.80
## NRO1.1 13 86 15.13 5.22 15.0 15.33 4.45 0 26 26 -0.37 0.03
## NRO2.1 14 86 18.50 5.87 19.0 18.57 6.67 5 29 24 -0.15 -0.72
## NRO3.1 15 86 19.97 4.53 20.0 19.89 4.45 10 30 20 0.13 -0.56
## NRO4.1 16 86 10.87 4.38 11.0 10.84 5.93 1 25 24 0.21 -0.19
## NRO5.1 17 86 20.27 5.18 20.0 20.37 5.93 9 30 21 -0.17 -0.58
## NRO6.1 18 86 17.28 4.21 18.0 17.37 2.97 5 25 20 -0.31 -0.23
## NRA1.1 19 86 14.13 4.25 14.0 14.20 4.45 4 23 19 -0.12 -0.73
## NRA2.1 20 86 13.88 4.74 14.0 13.90 4.45 1 25 24 -0.03 -0.04
## NRA3.1 21 86 19.60 3.97 19.0 19.50 2.97 10 30 20 0.28 -0.16
## NRA4.1 22 86 11.84 4.03 12.0 11.90 4.45 3 21 18 -0.05 -0.67
## NRA5.1 23 86 16.92 5.15 16.0 16.89 5.93 6 28 22 0.08 -0.90
## NRA6.1 24 86 21.53 3.69 21.5 21.49 3.71 13 30 17 0.15 -0.31
## NRC1.1 25 86 15.02 4.30 15.0 14.99 4.45 6 27 21 0.16 -0.43
## NRC2.1 26 86 9.41 4.85 9.5 9.30 5.19 0 20 20 0.12 -0.72
## NRC3.1 27 86 18.74 4.89 19.0 18.53 4.45 9 32 23 0.42 0.18
## NRC4.1 28 86 12.14 5.49 12.0 11.90 5.93 1 26 25 0.34 -0.27
## NRC5.1 29 86 8.14 4.76 7.5 7.91 5.19 0 20 20 0.40 -0.62
## NRC6.1 30 86 14.26 5.42 14.0 14.10 5.93 4 29 25 0.27 -0.32
## se Q0.25 Q0.75
## NRN1.1 0.52 15.00 21.00
## NRN2.1 0.51 12.00 18.00
## NRN3.1 0.46 22.00 27.00
## NRN4.1 0.56 17.00 24.00
## NRN5.1 0.53 13.00 20.00
## NRN6.1 0.46 10.00 16.75
## NRE1.1 0.53 16.00 22.00
## NRE2.1 0.60 6.00 14.00
## NRE3.1 0.55 6.00 13.00
## NRE4.1 0.47 9.25 15.00
## NRE5.1 0.52 15.00 20.00
## NRE6.1 0.55 6.00 13.00
## NRO1.1 0.56 12.00 19.00
## NRO2.1 0.63 14.00 22.00
## NRO3.1 0.49 17.00 23.00
## NRO4.1 0.47 7.00 14.00
## NRO5.1 0.56 17.00 24.00
## NRO6.1 0.45 15.00 20.00
## NRA1.1 0.46 11.00 17.00
## NRA2.1 0.51 11.00 17.00
## NRA3.1 0.43 17.00 22.00
## NRA4.1 0.44 9.00 15.00
## NRA5.1 0.56 13.00 21.00
## NRA6.1 0.40 19.00 24.00
## NRC1.1 0.46 12.00 18.00
## NRC2.1 0.52 5.25 13.00
## NRC3.1 0.53 15.00 21.75
## NRC4.1 0.59 8.00 15.75
## NRC5.1 0.51 4.00 11.00
## NRC6.1 0.58 10.00 18.00ffm.names = names(neo.depr.bdi[,c(1:30)])
for(i in 1:30){
hist(neo.depr.bdi[,i], xlab = ffm.names[i], main = ffm.names[i])
densityPlot(neo.depr.bdi[,i], xlab = ffm.names[i], main = ffm.names[i])
}
## Time 1 and Time 2 BDI-II
describe(neo.depr.bdi$BDI2.1, quant = c(.25, .75)) # Pre-tx## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 208 30.21 8.51 30 29.87 7.41 8 60 52 0.44 0.59 0.59
## Q0.25 Q0.75
## 1 24.75 35hist(neo.depr.bdi$BDI2.1)
densityPlot(neo.depr.bdi$BDI2.1)
describe(neo.depr.bdi$BDI2.2, quant = c(.25, .75)) # Post-tx## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 16.22 11.27 14 15.32 11.86 0 51 51 0.71 -0.07
## se Q0.25 Q0.75
## 1 0.78 7 23hist(neo.depr.bdi$BDI2.2)
densityPlot(neo.depr.bdi$BDI2.2)
describeBy(neo.depr.bdi$BDI2.1, group = neo.depr.bdi$ccode, quant = c(.25, .75))##
## Descriptive statistics by group
## group: UPC
## NULL
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 45 31.45 10.87 30 30.82 8.9 13 60 47 0.57 0.02 1.62
## Q0.25 Q0.75
## 1 25 37
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 77 30.09 7.46 29.97 29.56 7.37 17 52 35 0.72 0.34 0.85
## Q0.25 Q0.75
## 1 25 35
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 86 29.66 8.03 30.5 29.79 8.15 8 48 40 -0.18 -0.18 0.87
## Q0.25 Q0.75
## 1 24 34.75describeBy(neo.depr.bdi$BDI2.2, group = neo.depr.bdi$ccode, quant = c(.25, .75))##
## Descriptive statistics by group
## group: UPC
## NULL
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 45 19.87 12.88 17 18.87 13.34 1 51 50 0.65 -0.52
## se Q0.25 Q0.75
## 1 1.92 11 28
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 77 18.61 9.33 17 18.27 8.9 0 44 44 0.4 -0.59 1.06
## Q0.25 Q0.75
## 1 12 26
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 86 12.17 10.82 10 10.7 10.38 0 44 44 1.12 0.71
## se Q0.25 Q0.75
## 1 1.17 3.25 16.753.3 Participants with full HAM-D data
## Sample size
nrow(neo.depr.hd)## [1] 325## Study
table(neo.depr.hd$ccode)##
## UPC NPL OMHF Quilty et al.
## 127 40 75 83## Tx group
table(neo.depr.hd$txgrp)##
## 0 1
## 84 241table(neo.depr.hd$ccode, neo.depr.hd$txgrp)##
## 0 1
## UPC 0 127
## NPL 0 40
## OMHF 39 36
## Quilty et al. 45 38## Sex
freq(neo.depr.hd$sex)## Frequencies
## neo.depr.hd$sex
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------- ------ --------- -------------- --------- --------------
## 0 127 39.08 39.08 39.08 39.08
## 1 198 60.92 100.00 60.92 100.00
## <NA> 0 0.00 100.00
## Total 325 100.00 100.00 100.00 100.00## Age
describe(neo.depr.hd$age, quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 325 38.2 11.33 38 37.89 14.83 18 64 46 0.21 -1.06
## se Q0.25 Q0.75
## 1 0.63 28 48hist(neo.depr.hd$age)
densityPlot(neo.depr.hd$age)
describeBy(neo.depr.hd$age, group = neo.depr.hd$ccode, quant = c(.25, .75))##
## Descriptive statistics by group
## group: UPC
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 127 39.93 11.13 40 39.91 13.34 19 64 45 0.01 -1
## se Q0.25 Q0.75
## 1 0.99 30.5 49
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 40 36.6 11.75 34 36.12 14.83 20 62 42 0.35 -1.17 1.86
## Q0.25 Q0.75
## 1 27 48
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 75 41.12 11.55 40 41.11 14.83 21 62 41 0.01 -1.15
## se Q0.25 Q0.75
## 1 1.33 31 52
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 83 33.7 9.81 30 33.04 8.9 18 59 41 0.59 -0.74 1.08
## Q0.25 Q0.75
## 1 26 42## FFM facets
describe(neo.depr.hd[,c(1:30)], quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew
## NRN1.1 1 325 20.76 5.31 21 20.87 5.93 4 32 28 -0.23
## NRN2.1 2 325 17.22 5.39 17 17.05 5.93 5 31 26 0.23
## NRN3.1 3 325 24.57 4.27 25 24.77 4.45 9 32 23 -0.49
## NRN4.1 4 325 20.36 5.38 21 20.45 5.93 6 32 26 -0.16
## NRN5.1 5 325 18.14 5.25 18 18.14 4.45 3 32 29 0.03
## NRN6.1 6 325 17.13 5.31 17 17.13 5.93 2 32 30 -0.03
## NRE1.1 7 325 18.23 5.31 19 18.41 4.45 3 32 29 -0.30
## NRE2.1 8 325 12.62 5.73 13 12.62 5.93 0 30 30 0.10
## NRE3.1 9 325 11.94 5.45 12 11.81 5.93 0 27 27 0.20
## NRE4.1 10 325 13.61 4.82 13 13.36 4.45 1 31 30 0.41
## NRE5.1 11 325 15.48 5.00 16 15.54 4.45 2 30 28 -0.08
## NRE6.1 12 325 11.91 5.77 12 11.82 5.93 0 26 26 0.13
## NRO1.1 13 325 17.56 5.54 17 17.48 5.93 0 32 32 0.07
## NRO2.1 14 325 18.50 5.89 19 18.73 5.93 2 32 30 -0.35
## NRO3.1 15 325 20.86 4.79 21 20.98 4.45 8 32 24 -0.23
## NRO4.1 16 325 14.12 4.71 14 14.18 4.45 1 26 25 -0.13
## NRO5.1 17 325 19.20 6.03 20 19.31 5.93 5 32 27 -0.20
## NRO6.1 18 325 20.91 4.49 21 21.09 4.45 5 31 26 -0.39
## NRA1.1 19 325 16.07 5.55 16 16.15 5.93 0 31 31 -0.10
## NRA2.1 20 325 18.98 5.44 20 19.20 5.93 1 31 30 -0.38
## NRA3.1 21 325 21.40 4.38 21 21.51 4.45 8 32 24 -0.22
## NRA4.1 22 325 15.90 5.11 16 15.95 5.93 3 30 27 -0.09
## NRA5.1 23 325 20.24 5.04 20 20.41 4.45 6 32 26 -0.30
## NRA6.1 24 325 21.17 3.92 21 21.22 4.45 10 31 21 -0.10
## NRC1.1 25 325 16.93 5.02 17 17.02 4.45 5 31 26 -0.13
## NRC2.1 26 325 14.52 5.87 15 14.72 7.41 0 28 28 -0.23
## NRC3.1 27 325 19.71 4.55 20 19.73 4.45 8 32 24 -0.02
## NRC4.1 28 325 14.22 5.75 14 14.11 5.93 1 31 30 0.21
## NRC5.1 29 325 12.35 6.04 12 12.27 5.93 0 28 28 0.12
## NRC6.1 30 325 15.94 5.11 16 15.94 4.45 3 30 27 0.02
## kurtosis se Q0.25 Q0.75
## NRN1.1 -0.19 0.29 17 25
## NRN2.1 -0.63 0.30 13 21
## NRN3.1 0.07 0.24 22 28
## NRN4.1 -0.56 0.30 17 24
## NRN5.1 -0.13 0.29 15 21
## NRN6.1 -0.37 0.29 13 21
## NRE1.1 -0.15 0.29 15 22
## NRE2.1 -0.02 0.32 9 17
## NRE3.1 -0.62 0.30 8 16
## NRE4.1 -0.02 0.27 10 16
## NRE5.1 -0.15 0.28 12 19
## NRE6.1 -0.60 0.32 8 16
## NRO1.1 -0.10 0.31 14 21
## NRO2.1 -0.26 0.33 15 23
## NRO3.1 -0.16 0.27 18 24
## NRO4.1 -0.34 0.26 11 17
## NRO5.1 -0.82 0.33 15 24
## NRO6.1 0.13 0.25 18 24
## NRA1.1 -0.32 0.31 12 20
## NRA2.1 -0.21 0.30 15 23
## NRA3.1 -0.28 0.24 18 25
## NRA4.1 -0.48 0.28 12 20
## NRA5.1 -0.14 0.28 17 24
## NRA6.1 -0.16 0.22 18 24
## NRC1.1 -0.37 0.28 13 20
## NRC2.1 -0.62 0.33 10 19
## NRC3.1 -0.28 0.25 17 23
## NRC4.1 -0.20 0.32 10 18
## NRC5.1 -0.71 0.34 8 17
## NRC6.1 -0.28 0.28 13 19describeBy(neo.depr.hd[,c(1:30)], group = neo.depr.hd$ccode, quant = c(.25, .75))##
## Descriptive statistics by group
## group: UPC
## vars n mean sd median trimmed mad min max range skew
## NRN1.1 1 127 22.47 4.83 22 22.61 5.93 11 32 21 -0.22
## NRN2.1 2 127 18.46 5.37 19 18.39 7.41 8 31 23 0.10
## NRN3.1 3 127 25.39 4.18 26 25.68 4.45 9 32 23 -0.81
## NRN4.1 4 127 21.33 5.45 22 21.47 5.93 9 32 23 -0.23
## NRN5.1 5 127 19.09 5.04 19 19.08 4.45 6 32 26 0.03
## NRN6.1 6 127 19.54 5.10 20 19.72 5.93 2 32 30 -0.37
## NRE1.1 7 127 18.08 5.37 19 18.46 5.93 3 29 26 -0.58
## NRE2.1 8 127 13.22 5.46 13 13.22 5.93 0 29 29 0.07
## NRE3.1 9 127 12.59 5.34 12 12.46 5.93 2 27 25 0.22
## NRE4.1 10 127 14.15 4.77 14 13.84 4.45 6 26 20 0.47
## NRE5.1 11 127 14.57 4.91 15 14.58 4.45 4 26 22 0.01
## NRE6.1 12 127 12.10 5.85 11 12.03 5.93 1 26 25 0.13
## NRO1.1 13 127 18.15 5.53 18 17.92 5.93 7 32 25 0.38
## NRO2.1 14 127 18.30 5.67 18 18.51 5.93 2 32 30 -0.35
## NRO3.1 15 127 21.28 4.89 21 21.39 4.45 8 32 24 -0.25
## NRO4.1 16 127 14.94 4.20 15 15.06 4.45 3 24 21 -0.29
## NRO5.1 17 127 18.03 6.25 19 18.08 7.41 5 32 27 -0.08
## NRO6.1 18 127 22.16 3.86 22 22.17 4.45 11 31 20 -0.19
## NRA1.1 19 127 16.39 6.01 17 16.64 7.41 0 31 31 -0.28
## NRA2.1 20 127 21.24 4.74 22 21.48 4.45 8 31 23 -0.51
## NRA3.1 21 127 21.79 4.64 23 22.08 4.45 8 31 23 -0.58
## NRA4.1 22 127 17.37 4.79 18 17.60 4.45 3 27 24 -0.49
## NRA5.1 23 127 21.78 4.95 22 21.98 4.45 6 32 26 -0.47
## NRA6.1 24 127 21.15 4.14 21 21.21 4.45 10 30 20 -0.19
## NRC1.1 25 127 16.93 5.22 17 17.05 5.93 5 28 23 -0.25
## NRC2.1 26 127 16.09 5.30 16 16.26 5.93 3 28 25 -0.25
## NRC3.1 27 127 20.25 4.55 20 20.42 4.45 8 30 22 -0.29
## NRC4.1 28 127 14.90 5.82 14 14.77 5.93 3 29 26 0.23
## NRC5.1 29 127 13.63 5.94 14 13.67 5.93 1 28 27 0.01
## NRC6.1 30 127 16.34 4.70 16 16.44 4.45 4 26 22 -0.22
## kurtosis se Q0.25 Q0.75
## NRN1.1 -0.61 0.43 19.0 26.0
## NRN2.1 -0.89 0.48 14.0 23.0
## NRN3.1 0.98 0.37 23.0 29.0
## NRN4.1 -0.79 0.48 17.0 25.5
## NRN5.1 -0.16 0.45 16.0 22.5
## NRN6.1 0.06 0.45 16.0 23.0
## NRE1.1 -0.07 0.48 15.0 22.0
## NRE2.1 0.14 0.48 9.5 17.0
## NRE3.1 -0.77 0.47 8.0 17.0
## NRE4.1 -0.64 0.42 10.0 17.0
## NRE5.1 -0.58 0.44 11.0 18.0
## NRE6.1 -0.61 0.52 8.0 16.0
## NRO1.1 -0.58 0.49 14.0 22.0
## NRO2.1 0.04 0.50 15.0 22.5
## NRO3.1 -0.05 0.43 18.0 25.0
## NRO4.1 -0.21 0.37 12.0 18.0
## NRO5.1 -1.04 0.55 13.0 23.0
## NRO6.1 0.35 0.34 20.0 25.0
## NRA1.1 -0.53 0.53 11.5 21.0
## NRA2.1 0.00 0.42 19.0 24.5
## NRA3.1 0.02 0.41 19.0 25.0
## NRA4.1 -0.10 0.43 14.5 21.0
## NRA5.1 0.48 0.44 19.5 25.0
## NRA6.1 -0.15 0.37 18.0 24.0
## NRC1.1 -0.43 0.46 14.0 21.0
## NRC2.1 -0.53 0.47 12.0 20.0
## NRC3.1 -0.48 0.40 17.0 23.0
## NRC4.1 -0.43 0.52 11.0 19.0
## NRC5.1 -0.56 0.53 10.0 18.0
## NRC6.1 -0.41 0.42 13.0 20.0
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis
## NRN1.1 1 40 21.73 4.66 22.0 21.62 5.93 14 30 16 0.06 -1.12
## NRN2.1 2 40 18.10 5.85 18.5 18.09 5.93 5 30 25 -0.08 -0.69
## NRN3.1 3 40 23.75 4.69 25.0 23.94 5.19 12 32 20 -0.39 -0.70
## NRN4.1 4 40 19.50 5.26 20.0 19.66 5.19 7 29 22 -0.24 -0.66
## NRN5.1 5 40 17.95 5.92 17.0 17.72 5.93 6 31 25 0.32 -0.40
## NRN6.1 6 40 18.02 4.78 18.0 17.91 4.45 10 27 17 0.25 -0.91
## NRE1.1 7 40 18.12 6.21 19.0 18.19 7.41 5 30 25 -0.04 -0.80
## NRE2.1 8 40 12.32 5.57 12.5 12.28 5.19 0 25 25 0.02 -0.28
## NRE3.1 9 40 13.07 4.98 13.5 13.03 5.93 3 24 21 0.12 -0.85
## NRE4.1 10 40 14.05 5.53 14.0 13.62 4.45 4 31 27 0.71 0.74
## NRE5.1 11 40 14.75 4.89 15.0 14.84 4.45 5 23 18 -0.15 -0.81
## NRE6.1 12 40 12.82 6.27 12.0 12.66 6.67 0 26 26 0.22 -0.85
## NRO1.1 13 40 19.12 5.28 19.0 19.25 5.93 8 29 21 -0.08 -0.79
## NRO2.1 14 40 20.00 6.01 20.5 20.12 5.93 7 31 24 -0.14 -0.65
## NRO3.1 15 40 21.27 5.53 22.0 21.56 4.45 8 31 23 -0.49 -0.19
## NRO4.1 16 40 14.70 4.34 14.0 14.34 3.71 5 26 21 0.65 0.56
## NRO5.1 17 40 20.50 7.18 23.0 20.69 8.15 8 31 23 -0.23 -1.37
## NRO6.1 18 40 21.95 4.11 22.0 22.06 4.45 14 29 15 -0.14 -0.94
## NRA1.1 19 40 17.15 5.93 17.5 17.19 6.67 3 31 28 -0.08 -0.29
## NRA2.1 20 40 20.43 3.51 20.0 20.44 4.45 14 28 14 0.06 -1.00
## NRA3.1 21 40 23.35 4.33 24.0 23.56 4.45 13 32 19 -0.43 -0.38
## NRA4.1 22 40 17.45 4.63 17.0 17.41 4.45 5 26 21 -0.07 -0.14
## NRA5.1 23 40 21.52 4.49 22.5 21.84 5.19 8 29 21 -0.77 0.43
## NRA6.1 24 40 22.23 3.72 23.0 22.34 2.97 12 31 19 -0.31 0.62
## NRC1.1 25 40 19.35 5.55 20.0 19.56 4.45 6 31 25 -0.39 -0.06
## NRC2.1 26 40 17.27 4.71 18.0 17.53 2.97 5 26 21 -0.57 0.04
## NRC3.1 27 40 19.32 4.67 20.0 19.44 5.93 9 29 20 -0.19 -0.69
## NRC4.1 28 40 14.75 6.91 15.0 14.50 7.41 3 31 28 0.21 -0.58
## NRC5.1 29 40 14.25 5.63 15.5 14.50 5.19 1 24 23 -0.42 -0.75
## NRC6.1 30 40 17.98 5.69 18.0 18.03 4.45 5 30 25 0.00 -0.37
## se Q0.25 Q0.75
## NRN1.1 0.74 17.75 24.25
## NRN2.1 0.93 13.75 22.00
## NRN3.1 0.74 19.75 27.00
## NRN4.1 0.83 16.00 23.00
## NRN5.1 0.94 13.75 21.25
## NRN6.1 0.76 14.75 21.00
## NRE1.1 0.98 13.75 23.00
## NRE2.1 0.88 9.00 16.00
## NRE3.1 0.79 9.00 16.25
## NRE4.1 0.87 10.00 16.25
## NRE5.1 0.77 11.75 18.00
## NRE6.1 0.99 8.75 17.50
## NRO1.1 0.83 15.00 23.00
## NRO2.1 0.95 16.75 23.75
## NRO3.1 0.87 18.75 24.00
## NRO4.1 0.69 12.00 17.00
## NRO5.1 1.13 14.75 26.25
## NRO6.1 0.65 19.00 25.25
## NRA1.1 0.94 12.75 21.25
## NRA2.1 0.56 18.00 23.25
## NRA3.1 0.68 21.00 26.00
## NRA4.1 0.73 14.00 20.25
## NRA5.1 0.71 18.75 24.00
## NRA6.1 0.59 20.75 24.25
## NRC1.1 0.88 16.75 23.00
## NRC2.1 0.75 14.00 20.00
## NRC3.1 0.74 16.00 23.25
## NRC4.1 1.09 10.00 18.50
## NRC5.1 0.89 10.50 18.00
## NRC6.1 0.90 15.00 21.25
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis
## NRN1.1 1 75 20.83 5.44 20 20.85 5.93 7 32 25 -0.05 -0.44
## NRN2.1 2 75 17.07 5.38 17 16.79 5.93 6 29 23 0.34 -0.73
## NRN3.1 3 75 23.97 4.12 24 23.98 4.45 14 32 18 -0.04 -0.58
## NRN4.1 4 75 19.40 5.28 19 19.36 4.45 8 31 23 0.09 -0.39
## NRN5.1 5 75 18.83 5.08 19 18.79 5.93 8 32 24 0.12 -0.38
## NRN6.1 6 75 16.61 4.54 17 16.59 4.45 6 27 21 0.04 -0.67
## NRE1.1 7 75 17.79 5.29 17 17.84 5.93 6 30 24 -0.07 -0.43
## NRE2.1 8 75 13.97 5.98 14 14.07 5.93 1 30 29 -0.02 0.10
## NRE3.1 9 75 12.99 5.38 13 12.87 5.93 4 24 20 0.19 -0.88
## NRE4.1 10 75 14.45 4.54 14 14.31 4.45 6 24 18 0.26 -0.82
## NRE5.1 11 75 15.39 4.96 16 15.41 4.45 3 27 24 -0.13 -0.33
## NRE6.1 12 75 13.21 5.60 14 13.25 5.93 1 25 24 -0.08 -0.68
## NRO1.1 13 75 18.47 5.23 19 18.34 5.93 6 32 26 0.09 -0.24
## NRO2.1 14 75 18.20 6.20 19 18.59 5.93 3 29 26 -0.63 -0.37
## NRO3.1 15 75 20.97 4.43 22 21.20 4.45 9 29 20 -0.47 -0.09
## NRO4.1 16 75 16.07 4.23 16 16.28 4.45 6 25 19 -0.33 -0.55
## NRO5.1 17 75 19.43 5.66 20 19.59 5.93 7 30 23 -0.33 -0.58
## NRO6.1 18 75 22.35 3.63 23 22.43 2.97 12 30 18 -0.30 0.03
## NRA1.1 19 75 16.91 5.52 16 17.07 5.93 2 29 27 -0.25 -0.19
## NRA2.1 20 75 20.04 4.38 21 20.08 5.93 11 29 18 -0.06 -0.81
## NRA3.1 21 75 21.59 3.73 22 21.59 4.45 13 31 18 0.08 -0.46
## NRA4.1 22 75 16.89 4.83 16 16.90 5.93 6 30 24 0.09 -0.52
## NRA5.1 23 75 20.47 3.73 20 20.34 4.45 12 29 17 0.26 -0.20
## NRA6.1 24 75 20.25 3.70 20 20.31 2.97 13 28 15 -0.06 -0.72
## NRC1.1 25 75 17.77 4.42 18 18.00 4.45 7 25 18 -0.47 -0.28
## NRC2.1 26 75 16.13 4.94 17 16.26 5.93 4 27 23 -0.23 -0.62
## NRC3.1 27 75 19.97 3.95 20 19.92 4.45 11 30 19 0.16 -0.38
## NRC4.1 28 75 15.00 4.68 15 15.05 4.45 4 29 25 0.02 0.33
## NRC5.1 29 75 13.73 5.66 13 13.62 5.93 3 25 22 0.18 -0.88
## NRC6.1 30 75 16.03 4.75 16 16.05 4.45 3 28 25 -0.05 -0.22
## se Q0.25 Q0.75
## NRN1.1 0.63 17.5 25.0
## NRN2.1 0.62 13.0 21.0
## NRN3.1 0.48 21.0 27.0
## NRN4.1 0.61 16.0 23.0
## NRN5.1 0.59 16.0 23.0
## NRN6.1 0.52 13.0 20.0
## NRE1.1 0.61 14.5 22.0
## NRE2.1 0.69 11.0 18.0
## NRE3.1 0.62 8.5 17.0
## NRE4.1 0.52 11.0 18.0
## NRE5.1 0.57 11.0 18.0
## NRE6.1 0.65 8.0 17.0
## NRO1.1 0.60 14.0 22.0
## NRO2.1 0.72 15.0 22.5
## NRO3.1 0.51 18.0 24.0
## NRO4.1 0.49 13.5 19.0
## NRO5.1 0.65 16.0 23.0
## NRO6.1 0.42 20.5 24.0
## NRA1.1 0.64 13.5 21.0
## NRA2.1 0.51 16.5 23.0
## NRA3.1 0.43 19.0 24.5
## NRA4.1 0.56 14.0 21.0
## NRA5.1 0.43 18.0 23.0
## NRA6.1 0.43 18.0 23.5
## NRC1.1 0.51 15.5 21.0
## NRC2.1 0.57 13.0 20.0
## NRC3.1 0.46 17.0 22.0
## NRC4.1 0.54 12.0 18.0
## NRC5.1 0.65 10.0 18.0
## NRC6.1 0.55 13.0 19.0
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis
## NRN1.1 1 83 17.61 4.85 18 17.96 4.45 4 27 23 -0.56 -0.17
## NRN2.1 2 83 15.04 4.50 15 14.96 4.45 5 28 23 0.23 -0.24
## NRN3.1 3 83 24.25 4.21 25 24.42 4.45 12 32 20 -0.46 0.10
## NRN4.1 4 83 20.14 5.25 20 20.33 5.93 6 30 24 -0.33 -0.37
## NRN5.1 5 83 16.14 4.92 17 16.25 4.45 3 25 22 -0.30 -0.54
## NRN6.1 6 83 13.47 4.32 14 13.54 4.45 3 23 20 -0.18 -0.50
## NRE1.1 7 83 18.90 4.75 19 18.99 4.45 8 32 24 -0.09 0.02
## NRE2.1 8 83 10.61 5.51 11 10.49 5.93 0 27 27 0.27 -0.23
## NRE3.1 9 83 9.43 5.20 9 9.25 4.45 0 22 22 0.35 -0.24
## NRE4.1 10 83 11.80 4.37 12 11.78 4.45 1 24 23 0.08 -0.10
## NRE5.1 11 83 17.31 4.82 17 17.36 2.97 2 30 28 -0.17 1.07
## NRE6.1 12 83 10.00 5.14 10 9.99 5.93 0 22 22 0.09 -0.81
## NRO1.1 13 83 15.07 5.25 15 15.27 4.45 0 26 26 -0.37 0.02
## NRO2.1 14 83 18.36 5.86 19 18.43 7.41 5 29 24 -0.16 -0.76
## NRO3.1 15 83 19.93 4.51 20 19.85 4.45 10 30 20 0.13 -0.51
## NRO4.1 16 83 10.81 4.43 11 10.76 5.93 1 25 24 0.24 -0.22
## NRO5.1 17 83 20.14 5.12 20 20.25 5.93 9 30 21 -0.20 -0.55
## NRO6.1 18 83 17.20 4.26 17 17.28 4.45 5 25 20 -0.26 -0.30
## NRA1.1 19 83 14.30 4.18 14 14.37 4.45 4 23 19 -0.11 -0.76
## NRA2.1 20 83 13.88 4.78 14 13.90 4.45 1 25 24 -0.04 -0.04
## NRA3.1 21 83 19.69 4.01 19 19.60 4.45 10 30 20 0.23 -0.22
## NRA4.1 22 83 12.00 3.97 12 12.06 4.45 4 21 17 -0.03 -0.73
## NRA5.1 23 83 17.05 5.09 16 17.03 5.93 6 28 22 0.08 -0.85
## NRA6.1 24 83 21.53 3.75 22 21.48 4.45 13 30 17 0.15 -0.40
## NRC1.1 25 83 15.01 4.26 15 14.96 4.45 6 27 21 0.21 -0.35
## NRC2.1 26 83 9.31 4.81 9 9.22 5.93 0 20 20 0.09 -0.73
## NRC3.1 27 83 18.82 4.91 19 18.61 4.45 9 32 23 0.41 0.20
## NRC4.1 28 83 12.20 5.54 12 11.97 5.93 1 26 25 0.32 -0.30
## NRC5.1 29 83 8.23 4.79 8 8.01 5.93 0 20 20 0.37 -0.66
## NRC6.1 30 83 14.25 5.31 14 14.06 5.93 5 29 24 0.33 -0.25
## se Q0.25 Q0.75
## NRN1.1 0.53 15.0 21.0
## NRN2.1 0.49 12.0 18.0
## NRN3.1 0.46 22.0 27.0
## NRN4.1 0.58 17.0 24.0
## NRN5.1 0.54 13.0 20.0
## NRN6.1 0.47 10.0 17.0
## NRE1.1 0.52 16.0 22.0
## NRE2.1 0.61 6.0 14.5
## NRE3.1 0.57 6.0 13.0
## NRE4.1 0.48 9.0 15.0
## NRE5.1 0.53 15.0 20.0
## NRE6.1 0.56 6.0 13.0
## NRO1.1 0.58 12.0 19.0
## NRO2.1 0.64 14.0 22.0
## NRO3.1 0.49 17.0 23.0
## NRO4.1 0.49 7.0 14.0
## NRO5.1 0.56 17.0 24.0
## NRO6.1 0.47 15.0 20.0
## NRA1.1 0.46 11.0 17.0
## NRA2.1 0.52 11.0 17.0
## NRA3.1 0.44 17.0 22.0
## NRA4.1 0.44 9.0 15.0
## NRA5.1 0.56 13.5 21.0
## NRA6.1 0.41 19.0 24.0
## NRC1.1 0.47 12.0 18.0
## NRC2.1 0.53 5.0 13.0
## NRC3.1 0.54 15.0 21.5
## NRC4.1 0.61 8.0 16.0
## NRC5.1 0.53 4.0 11.5
## NRC6.1 0.58 10.0 18.0for(i in 1:30){
hist(neo.depr.hd[,i], xlab = ffm.names[i], main = ffm.names[i])
densityPlot(neo.depr.hd[,i], xlab = ffm.names[i], main = ffm.names[i])
}
## Time 1 and Time 2 HAM-D
describe(neo.depr.hd$hamd1, quant = c(.25, .75)) # Pre-tx## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 325 19.32 5.07 19 19.26 4.45 4 40 36 0.27 0.79 0.28
## Q0.25 Q0.75
## 1 16 23hist(neo.depr.hd$hamd1)
densityPlot(neo.depr.hd$hamd1)
describe(neo.depr.hd$hamd2, quant = c(.25, .75)) # Post-tx## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 325 8.82 7.36 7 8.12 7.41 0 33 33 0.72 -0.34 0.41
## Q0.25 Q0.75
## 1 3 14hist(neo.depr.hd$hamd2)
densityPlot(neo.depr.hd$hamd2)
describeBy(neo.depr.hd$hamd1, group = neo.depr.hd$ccode, quant = c(.25, .75))##
## Descriptive statistics by group
## group: UPC
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 127 21.98 4.51 21 21.62 4.45 14 40 26 0.9 1.3 0.4
## Q0.25 Q0.75
## 1 18 25
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 40 19.48 5.33 19.5 19.41 5.19 9 31 22 0.13 -0.95 0.84
## Q0.25 Q0.75
## 1 16 24.25
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 75 17.79 3.26 17 17.61 2.97 10 26 16 0.45 -0.1 0.38
## Q0.25 Q0.75
## 1 16 19
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 83 16.58 5.15 16 16.61 4.45 4 28 24 -0.07 -0.37 0.57
## Q0.25 Q0.75
## 1 13.5 20describeBy(neo.depr.hd$hamd2, group = neo.depr.hd$ccode, quant = c(.25, .75))##
## Descriptive statistics by group
## group: UPC
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 127 10.09 8.25 9 9.41 10.38 0 33 33 0.58 -0.65
## se Q0.25 Q0.75
## 1 0.73 3 17
## --------------------------------------------------------
## group: NPL
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 40 12.07 7.23 12.5 11.88 8.9 0 25 25 0.07 -1.17 1.14
## Q0.25 Q0.75
## 1 5 18
## --------------------------------------------------------
## group: OMHF
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 75 5.91 5.7 4 5.16 4.45 0 22 22 1.05 0.19 0.66
## Q0.25 Q0.75
## 1 1 9
## --------------------------------------------------------
## group: Quilty et al.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 83 7.93 6.26 7 7.36 7.41 0 26 26 0.68 -0.31 0.69
## Q0.25 Q0.75
## 1 3 124 Preliminary inferential statistics
4.1 Functions
# Functions for bootstrapping Spearman's correlations for CIs
spearman.boot = function(data, indices, x, y){
d = data[indices, ]
cor = cor.test(d[,c(x)], d[,c(y)], method = "spearman")$estimate # Getting the r estimate
return(cor)
}
spearman = function(data, x, y, xlab, ylab){
cor.out = cor.test(data[,c(x)], data[,c(y)], method = "spearman")
print(cor.out) # Print the actual esimate and p-value
set.seed(2020)
boot = boot(data, statistic = spearman.boot, R = 1000, x = x, y = y) %>% boot.ci(type = "bca")
print(boot) # Print BCa CIs
scatter.hist(data[,c(x)], data[,c(y)], method = "spearman", ellipse = F, ab = T, xlab = xlab, ylab = ylab)
}
# Function for paired t-tests and bootstrapping CIs for cohen's dz
cohen.dz.boot = function(data, indices, t1, t2, n){
d = data[indices, ]
statistic = t.test(d[,c(t1)], d[,c(t2)])$statistic
dz = sqrt(statistic/n) # Cohen's dz
return(dz)
}
paired = function(data, t1, t2, n){
test = t.test(data[,c(t1)], data[,c(t2)], paired = T)
print(test)
statistic = test$statistic
dz = sqrt(statistic/n)
print(dz)
set.seed(2020)
boot = boot(data, statistic = cohen.dz.boot, R = 1000, t1 = t1, t2 = t2, n = n) %>% boot.ci(type = "bca")
print(boot) # Bootstrapped cohen's dz
}4.2 Rank-order correlation between pre- and post-tx depression scores
spearman(neo.depr.bdi, x = "BDI2.1", y = "BDI2.2", xlab = "BDI-II Pre", ylab = "BDI-II Post")##
## Spearman's rank correlation rho
##
## data: data[, c(x)] and data[, c(y)]
## S = 1066900, p-value = 2.357e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.288641
##
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% ( 0.1544, 0.4205 )
## Calculations and Intervals on Original Scale
spearman(neo.depr.full, x = "hamd1", y = "hamd2", xlab = "HAM-D Pre", ylab = "HAM-D Post")##
## Spearman's rank correlation rho
##
## data: data[, c(x)] and data[, c(y)]
## S = 4292400, p-value = 6.818e-07
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2701442
##
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% ( 0.1624, 0.3762 )
## Calculations and Intervals on Original Scale
4.3 Mean-level change in depression scores from pre-tx to post-tx
# Across treatment type
paired(data = neo.depr.bdi, t1 = "BDI2.1", t2 = "BDI2.2", n = 208)##
## Paired t-test
##
## data: data[, c(t1)] and data[, c(t2)]
## t = 18.072, df = 207, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 12.45949 15.51086
## sample estimates:
## mean of the differences
## 13.98518
##
## t
## 0.2947589
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% ( 0.2419, 0.2805 )
## Calculations and Intervals on Original Scalepaired(data = neo.depr.hd, t1 = "hamd1", t2 = "hamd2", n = 325)##
## Paired t-test
##
## data: data[, c(t1)] and data[, c(t2)]
## t = 25.691, df = 324, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 9.70020 11.30903
## sample estimates:
## mean of the differences
## 10.50462
##
## t
## 0.2811545
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% ( 0.2415, 0.2687 )
## Calculations and Intervals on Original Scale4.4 Self-interview agreement between pre- and post-tx depression scores
spearman(neo.depr.full, x = "BDI2.1", y = "hamd1", xlab = "BDI-II Pre", ylab = "HAM-D Pre")##
## Spearman's rank correlation rho
##
## data: data[, c(x)] and data[, c(y)]
## S = 1007300, p-value = 2.456e-12
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.4476159
##
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% ( 0.3213, 0.5496 )
## Calculations and Intervals on Original Scale
spearman(neo.depr.full, x = "BDI2.2", y = "hamd2", xlab = "BDI-II Post", ylab = "HAM-D Post")##
## Spearman's rank correlation rho
##
## data: data[, c(x)] and data[, c(y)]
## S = 633010, p-value = 4.649e-15
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.518034
##
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% ( 0.3837, 0.6187 )
## Calculations and Intervals on Original Scale
spearman(neo.depr.full, x = "BDI2.1", y = "hamd2", xlab = "BDI-II Pre", ylab = "HAM-D Post")##
## Spearman's rank correlation rho
##
## data: data[, c(x)] and data[, c(y)]
## S = 1160800, p-value = 0.1023
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1161577
##
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% (-0.0136, 0.2601 )
## Calculations and Intervals on Original Scale
spearman(neo.depr.full, x = "hamd1", y = "BDI2.2", xlab = "HAM-D Pre", ylab = "BDI-II Post")##
## Spearman's rank correlation rho
##
## data: data[, c(x)] and data[, c(y)]
## S = 1033900, p-value = 9.847e-05
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2692597
##
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = ., type = "bca")
##
## Intervals :
## Level BCa
## 95% ( 0.1326, 0.3935 )
## Calculations and Intervals on Original Scale
## Correlations between each FFM facet and BDI-II scores
print(corr.test(neo.depr.bdi[,c(1:30)], neo.depr.bdi[,c(31:32)], method = "spearman", ci = T, alpha = .001), short = F)## Call:corr.test(x = neo.depr.bdi[, c(1:30)], y = neo.depr.bdi[, c(31:32)],
## method = "spearman", alpha = 0.001, ci = T)
## Correlation matrix
## BDI2.1 BDI2.2
## NRN1.1 0.31 0.28
## NRN2.1 0.21 0.11
## NRN3.1 0.41 0.14
## NRN4.1 0.32 0.12
## NRN5.1 0.28 0.05
## NRN6.1 0.30 0.18
## NRE1.1 -0.09 -0.15
## NRE2.1 -0.05 0.04
## NRE3.1 -0.10 0.16
## NRE4.1 -0.10 0.03
## NRE5.1 -0.05 -0.07
## NRE6.1 -0.25 -0.06
## NRO1.1 0.09 0.11
## NRO2.1 -0.04 0.01
## NRO3.1 0.05 0.06
## NRO4.1 -0.09 0.02
## NRO5.1 -0.03 -0.04
## NRO6.1 -0.02 0.12
## NRA1.1 -0.18 0.01
## NRA2.1 0.00 0.20
## NRA3.1 0.05 0.13
## NRA4.1 -0.09 0.16
## NRA5.1 0.15 0.20
## NRA6.1 0.01 -0.05
## NRC1.1 -0.19 0.16
## NRC2.1 -0.08 0.22
## NRC3.1 -0.14 0.07
## NRC4.1 -0.19 0.12
## NRC5.1 -0.22 0.07
## NRC6.1 -0.26 0.10
## Sample Size
## [1] 208
## Probability values adjusted for multiple tests.
## BDI2.1 BDI2.2
## NRN1.1 0.00 0.00
## NRN2.1 0.14 1.00
## NRN3.1 0.00 1.00
## NRN4.1 0.00 1.00
## NRN5.1 0.00 1.00
## NRN6.1 0.00 0.36
## NRE1.1 1.00 1.00
## NRE2.1 1.00 1.00
## NRE3.1 1.00 0.74
## NRE4.1 1.00 1.00
## NRE5.1 1.00 1.00
## NRE6.1 0.01 1.00
## NRO1.1 1.00 1.00
## NRO2.1 1.00 1.00
## NRO3.1 1.00 1.00
## NRO4.1 1.00 1.00
## NRO5.1 1.00 1.00
## NRO6.1 1.00 1.00
## NRA1.1 0.43 1.00
## NRA2.1 1.00 0.21
## NRA3.1 1.00 1.00
## NRA4.1 1.00 0.76
## NRA5.1 1.00 0.16
## NRA6.1 1.00 1.00
## NRC1.1 0.24 0.74
## NRC2.1 1.00 0.08
## NRC3.1 1.00 1.00
## NRC4.1 0.28 1.00
## NRC5.1 0.07 1.00
## NRC6.1 0.01 1.00
##
## Confidence intervals based upon normal theory. To get bootstrapped values, try cor.ci
## raw.lower raw.r raw.upper raw.p lower.adj upper.adj
## NRN1.-BDI2.1 0.09 0.31 0.50 0.00 0.02 0.55
## NRN2.-BDI2.1 -0.02 0.21 0.41 0.14 -0.09 0.47
## NRN3.-BDI2.1 0.21 0.41 0.58 0.00 0.14 0.63
## NRN4.-BDI2.1 0.10 0.32 0.51 0.00 0.03 0.56
## NRN5.-BDI2.1 0.06 0.28 0.48 0.00 -0.01 0.53
## NRN6.-BDI2.1 0.08 0.30 0.49 0.00 0.01 0.55
## NRE1.-BDI2.1 -0.31 -0.09 0.13 1.00 -0.37 0.19
## NRE2.-BDI2.1 -0.27 -0.05 0.18 1.00 -0.31 0.22
## NRE3.-BDI2.1 -0.32 -0.10 0.13 1.00 -0.37 0.18
## NRE4.-BDI2.1 -0.32 -0.10 0.13 1.00 -0.37 0.19
## NRE5.-BDI2.1 -0.27 -0.05 0.18 1.00 -0.32 0.22
## NRE6.-BDI2.1 -0.45 -0.25 -0.03 0.01 -0.51 0.04
## NRO1.-BDI2.1 -0.14 0.09 0.31 1.00 -0.20 0.36
## NRO2.-BDI2.1 -0.26 -0.04 0.19 1.00 -0.30 0.23
## NRO3.-BDI2.1 -0.18 0.05 0.27 1.00 -0.22 0.32
## NRO4.-BDI2.1 -0.31 -0.09 0.14 1.00 -0.36 0.20
## NRO5.-BDI2.1 -0.26 -0.03 0.19 1.00 -0.29 0.23
## NRO6.-BDI2.1 -0.25 -0.02 0.20 1.00 -0.28 0.23
## NRA1.-BDI2.1 -0.39 -0.18 0.05 0.43 -0.44 0.11
## NRA2.-BDI2.1 -0.22 0.00 0.23 1.00 -0.22 0.23
## NRA3.-BDI2.1 -0.18 0.05 0.27 1.00 -0.22 0.31
## NRA4.-BDI2.1 -0.31 -0.09 0.14 1.00 -0.36 0.20
## NRA5.-BDI2.1 -0.08 0.15 0.36 1.00 -0.15 0.41
## NRA6.-BDI2.1 -0.22 0.01 0.24 1.00 -0.24 0.26
## NRC1.-BDI2.1 -0.40 -0.19 0.03 0.24 -0.46 0.10
## NRC2.-BDI2.1 -0.30 -0.08 0.15 1.00 -0.35 0.20
## NRC3.-BDI2.1 -0.35 -0.14 0.09 1.00 -0.41 0.15
## NRC4.-BDI2.1 -0.40 -0.19 0.04 0.28 -0.45 0.10
## NRC5.-BDI2.1 -0.43 -0.22 0.00 0.07 -0.48 0.07
## NRC6.-BDI2.1 -0.46 -0.26 -0.04 0.01 -0.52 0.03
## NRN1.-BDI2.2 0.05 0.28 0.47 0.00 -0.02 0.52
## NRN2.-BDI2.2 -0.12 0.11 0.33 1.00 -0.18 0.38
## NRN3.-BDI2.2 -0.09 0.14 0.35 1.00 -0.15 0.41
## NRN4.-BDI2.2 -0.11 0.12 0.34 1.00 -0.17 0.39
## NRN5.-BDI2.2 -0.18 0.05 0.27 1.00 -0.22 0.32
## NRN6.-BDI2.2 -0.04 0.18 0.39 0.36 -0.11 0.45
## NRE1.-BDI2.2 -0.36 -0.15 0.08 1.00 -0.42 0.14
## NRE2.-BDI2.2 -0.19 0.04 0.27 1.00 -0.23 0.30
## NRE3.-BDI2.2 -0.06 0.16 0.38 0.74 -0.13 0.43
## NRE4.-BDI2.2 -0.20 0.03 0.26 1.00 -0.23 0.29
## NRE5.-BDI2.2 -0.29 -0.07 0.16 1.00 -0.34 0.21
## NRE6.-BDI2.2 -0.28 -0.06 0.17 1.00 -0.33 0.22
## NRO1.-BDI2.2 -0.12 0.11 0.33 1.00 -0.18 0.38
## NRO2.-BDI2.2 -0.22 0.01 0.23 1.00 -0.23 0.25
## NRO3.-BDI2.2 -0.17 0.06 0.28 1.00 -0.22 0.33
## NRO4.-BDI2.2 -0.21 0.02 0.25 1.00 -0.23 0.27
## NRO5.-BDI2.2 -0.27 -0.04 0.19 1.00 -0.31 0.23
## NRO6.-BDI2.2 -0.11 0.12 0.33 1.00 -0.17 0.39
## NRA1.-BDI2.2 -0.22 0.01 0.23 1.00 -0.24 0.25
## NRA2.-BDI2.2 -0.03 0.20 0.40 0.21 -0.10 0.46
## NRA3.-BDI2.2 -0.10 0.13 0.34 1.00 -0.17 0.40
## NRA4.-BDI2.2 -0.07 0.16 0.38 0.76 -0.13 0.43
## NRA5.-BDI2.2 -0.02 0.20 0.41 0.16 -0.09 0.47
## NRA6.-BDI2.2 -0.28 -0.05 0.17 1.00 -0.32 0.22
## NRC1.-BDI2.2 -0.06 0.16 0.38 0.74 -0.13 0.43
## NRC2.-BDI2.2 -0.01 0.22 0.42 0.08 -0.08 0.48
## NRC3.-BDI2.2 -0.16 0.07 0.29 1.00 -0.21 0.34
## NRC4.-BDI2.2 -0.11 0.12 0.34 1.00 -0.17 0.39
## NRC5.-BDI2.2 -0.15 0.07 0.30 1.00 -0.21 0.34
## NRC6.-BDI2.2 -0.13 0.10 0.32 1.00 -0.19 0.374.5 Correlations between each FFM facet and HAM-D scores
print(corr.test(neo.depr.hd[,c(1:30)], neo.depr.hd[,c(31:32)], method = "spearman", ci = T, alpha = .001), short = F)## Call:corr.test(x = neo.depr.hd[, c(1:30)], y = neo.depr.hd[, c(31:32)],
## method = "spearman", alpha = 0.001, ci = T)
## Correlation matrix
## hamd1 hamd2
## NRN1.1 0.33 0.19
## NRN2.1 0.13 0.12
## NRN3.1 0.13 0.10
## NRN4.1 0.11 0.08
## NRN5.1 0.10 0.00
## NRN6.1 0.27 0.07
## NRE1.1 0.07 -0.01
## NRE2.1 0.09 -0.10
## NRE3.1 0.07 0.07
## NRE4.1 0.06 -0.05
## NRE5.1 -0.09 -0.11
## NRE6.1 0.04 -0.13
## NRO1.1 0.02 -0.03
## NRO2.1 -0.01 0.00
## NRO3.1 0.08 0.02
## NRO4.1 0.01 -0.11
## NRO5.1 -0.10 -0.04
## NRO6.1 0.08 -0.06
## NRA1.1 0.07 -0.08
## NRA2.1 0.21 0.11
## NRA3.1 0.15 0.15
## NRA4.1 0.14 0.05
## NRA5.1 0.25 0.12
## NRA6.1 0.06 0.14
## NRC1.1 -0.03 0.03
## NRC2.1 0.16 0.15
## NRC3.1 0.07 0.09
## NRC4.1 0.07 0.13
## NRC5.1 0.11 0.09
## NRC6.1 -0.06 0.09
## Sample Size
## [1] 325
## Probability values adjusted for multiple tests.
## hamd1 hamd2
## NRN1.1 0.00 0.04
## NRN2.1 0.99 1.00
## NRN3.1 0.82 1.00
## NRN4.1 1.00 1.00
## NRN5.1 1.00 1.00
## NRN6.1 0.00 1.00
## NRE1.1 1.00 1.00
## NRE2.1 1.00 1.00
## NRE3.1 1.00 1.00
## NRE4.1 1.00 1.00
## NRE5.1 1.00 1.00
## NRE6.1 1.00 0.99
## NRO1.1 1.00 1.00
## NRO2.1 1.00 1.00
## NRO3.1 1.00 1.00
## NRO4.1 1.00 1.00
## NRO5.1 1.00 1.00
## NRO6.1 1.00 1.00
## NRA1.1 1.00 1.00
## NRA2.1 0.01 1.00
## NRA3.1 0.37 0.41
## NRA4.1 0.54 1.00
## NRA5.1 0.00 1.00
## NRA6.1 1.00 0.61
## NRC1.1 1.00 1.00
## NRC2.1 0.20 0.34
## NRC3.1 1.00 1.00
## NRC4.1 1.00 0.73
## NRC5.1 1.00 1.00
## NRC6.1 1.00 1.00
##
## Confidence intervals based upon normal theory. To get bootstrapped values, try cor.ci
## raw.lower raw.r raw.upper raw.p lower.adj upper.adj
## NRN1.-hamd1 0.16 0.33 0.49 0.00 0.11 0.53
## NRN2.-hamd1 -0.05 0.13 0.30 0.99 -0.11 0.35
## NRN3.-hamd1 -0.05 0.13 0.31 0.82 -0.10 0.35
## NRN4.-hamd1 -0.08 0.11 0.28 1.00 -0.13 0.33
## NRN5.-hamd1 -0.08 0.10 0.28 1.00 -0.13 0.32
## NRN6.-hamd1 0.10 0.27 0.43 0.00 0.04 0.48
## NRE1.-hamd1 -0.11 0.07 0.25 1.00 -0.16 0.29
## NRE2.-hamd1 -0.10 0.09 0.26 1.00 -0.14 0.31
## NRE3.-hamd1 -0.11 0.07 0.25 1.00 -0.16 0.29
## NRE4.-hamd1 -0.12 0.06 0.24 1.00 -0.16 0.28
## NRE5.-hamd1 -0.27 -0.09 0.09 1.00 -0.31 0.14
## NRE6.-hamd1 -0.14 0.04 0.22 1.00 -0.18 0.25
## NRO1.-hamd1 -0.16 0.02 0.20 1.00 -0.19 0.23
## NRO2.-hamd1 -0.19 -0.01 0.17 1.00 -0.22 0.19
## NRO3.-hamd1 -0.10 0.08 0.26 1.00 -0.15 0.30
## NRO4.-hamd1 -0.17 0.01 0.19 1.00 -0.19 0.21
## NRO5.-hamd1 -0.28 -0.10 0.08 1.00 -0.33 0.13
## NRO6.-hamd1 -0.10 0.08 0.26 1.00 -0.15 0.30
## NRA1.-hamd1 -0.11 0.07 0.25 1.00 -0.16 0.29
## NRA2.-hamd1 0.03 0.21 0.38 0.01 -0.02 0.43
## NRA3.-hamd1 -0.03 0.15 0.32 0.37 -0.09 0.37
## NRA4.-hamd1 -0.04 0.14 0.31 0.54 -0.09 0.36
## NRA5.-hamd1 0.07 0.25 0.41 0.00 0.01 0.46
## NRA6.-hamd1 -0.13 0.06 0.23 1.00 -0.16 0.27
## NRC1.-hamd1 -0.21 -0.03 0.15 1.00 -0.24 0.19
## NRC2.-hamd1 -0.02 0.16 0.33 0.20 -0.08 0.38
## NRC3.-hamd1 -0.12 0.07 0.24 1.00 -0.16 0.28
## NRC4.-hamd1 -0.11 0.07 0.25 1.00 -0.16 0.29
## NRC5.-hamd1 -0.07 0.11 0.29 1.00 -0.12 0.33
## NRC6.-hamd1 -0.24 -0.06 0.12 1.00 -0.28 0.16
## NRN1.-hamd2 0.00 0.19 0.35 0.04 -0.05 0.40
## NRN2.-hamd2 -0.07 0.12 0.29 1.00 -0.12 0.34
## NRN3.-hamd2 -0.09 0.10 0.27 1.00 -0.14 0.32
## NRN4.-hamd2 -0.11 0.08 0.26 1.00 -0.15 0.30
## NRN5.-hamd2 -0.18 0.00 0.19 1.00 -0.19 0.20
## NRN6.-hamd2 -0.11 0.07 0.25 1.00 -0.16 0.29
## NRE1.-hamd2 -0.19 -0.01 0.18 1.00 -0.20 0.19
## NRE2.-hamd2 -0.28 -0.10 0.08 1.00 -0.32 0.13
## NRE3.-hamd2 -0.12 0.07 0.24 1.00 -0.16 0.28
## NRE4.-hamd2 -0.23 -0.05 0.13 1.00 -0.26 0.17
## NRE5.-hamd2 -0.28 -0.11 0.08 1.00 -0.33 0.13
## NRE6.-hamd2 -0.30 -0.13 0.05 0.99 -0.35 0.11
## NRO1.-hamd2 -0.21 -0.03 0.16 1.00 -0.24 0.18
## NRO2.-hamd2 -0.18 0.00 0.18 1.00 -0.18 0.18
## NRO3.-hamd2 -0.16 0.02 0.20 1.00 -0.19 0.23
## NRO4.-hamd2 -0.29 -0.11 0.07 1.00 -0.34 0.12
## NRO5.-hamd2 -0.22 -0.04 0.14 1.00 -0.26 0.17
## NRO6.-hamd2 -0.24 -0.06 0.13 1.00 -0.27 0.16
## NRA1.-hamd2 -0.26 -0.08 0.11 1.00 -0.30 0.15
## NRA2.-hamd2 -0.07 0.11 0.29 1.00 -0.12 0.33
## NRA3.-hamd2 -0.03 0.15 0.32 0.41 -0.09 0.37
## NRA4.-hamd2 -0.13 0.05 0.23 1.00 -0.17 0.27
## NRA5.-hamd2 -0.06 0.12 0.30 1.00 -0.11 0.35
## NRA6.-hamd2 -0.04 0.14 0.31 0.61 -0.10 0.36
## NRC1.-hamd2 -0.15 0.03 0.21 1.00 -0.19 0.24
## NRC2.-hamd2 -0.03 0.15 0.32 0.34 -0.09 0.37
## NRC3.-hamd2 -0.09 0.09 0.27 1.00 -0.14 0.31
## NRC4.-hamd2 -0.05 0.13 0.31 0.73 -0.10 0.36
## NRC5.-hamd2 -0.09 0.09 0.27 1.00 -0.14 0.31
## NRC6.-hamd2 -0.09 0.09 0.27 1.00 -0.14 0.324.6 Correlation matrix of FFM facets
# BDI-II participants
corr.test(neo.depr.bdi[,c(1:30)], method = "spearman")$r %>% round(2)## NRN1.1 NRN2.1 NRN3.1 NRN4.1 NRN5.1 NRN6.1 NRE1.1 NRE2.1 NRE3.1
## NRN1.1 1.00 0.42 0.41 0.50 0.25 0.58 -0.09 0.03 -0.07
## NRN2.1 0.42 1.00 0.15 0.26 0.35 0.43 -0.21 -0.06 0.06
## NRN3.1 0.41 0.15 1.00 0.64 0.24 0.46 -0.13 -0.16 -0.25
## NRN4.1 0.50 0.26 0.64 1.00 0.26 0.50 -0.21 -0.18 -0.37
## NRN5.1 0.25 0.35 0.24 0.26 1.00 0.29 0.01 0.04 0.02
## NRN6.1 0.58 0.43 0.46 0.50 0.29 1.00 -0.25 -0.03 -0.26
## NRE1.1 -0.09 -0.21 -0.13 -0.21 0.01 -0.25 1.00 0.53 0.34
## NRE2.1 0.03 -0.06 -0.16 -0.18 0.04 -0.03 0.53 1.00 0.38
## NRE3.1 -0.07 0.06 -0.25 -0.37 0.02 -0.26 0.34 0.38 1.00
## NRE4.1 0.03 0.10 -0.17 -0.16 0.02 -0.12 0.25 0.27 0.47
## NRE5.1 -0.13 0.03 -0.12 -0.03 0.19 -0.22 0.24 0.25 0.13
## NRE6.1 -0.06 -0.10 -0.25 -0.22 0.16 -0.20 0.54 0.48 0.40
## NRO1.1 0.20 0.21 0.10 0.07 0.35 0.17 0.08 0.09 0.14
## NRO2.1 0.13 0.08 0.07 0.04 0.09 0.02 0.30 0.16 0.18
## NRO3.1 0.21 0.22 0.13 -0.01 0.20 0.06 0.32 0.19 0.27
## NRO4.1 -0.07 -0.07 -0.17 -0.28 0.14 -0.09 0.25 0.35 0.32
## NRO5.1 -0.11 -0.04 -0.01 -0.08 0.03 -0.26 0.24 0.04 0.28
## NRO6.1 0.09 0.01 0.04 -0.10 0.17 0.02 0.02 0.16 0.19
## NRA1.1 -0.17 -0.31 -0.22 -0.30 -0.15 -0.15 0.35 0.26 0.32
## NRA2.1 0.07 -0.07 -0.01 -0.11 -0.02 0.24 0.03 0.11 0.10
## NRA3.1 0.06 -0.21 -0.02 -0.17 -0.07 -0.06 0.45 0.23 0.23
## NRA4.1 0.10 -0.39 0.04 0.01 -0.02 0.14 0.09 0.18 -0.02
## NRA5.1 0.21 -0.01 0.26 0.21 -0.03 0.42 -0.15 -0.03 -0.28
## NRA6.1 0.06 -0.09 0.15 0.02 0.02 -0.01 0.22 0.05 0.03
## NRC1.1 -0.11 -0.22 -0.35 -0.40 -0.16 -0.35 0.17 0.08 0.45
## NRC2.1 0.12 0.13 -0.12 -0.08 0.06 0.17 -0.10 0.14 0.27
## NRC3.1 -0.02 -0.18 -0.19 -0.19 -0.25 -0.11 0.18 0.11 0.23
## NRC4.1 0.01 0.02 -0.20 -0.17 -0.06 -0.19 0.24 0.09 0.44
## NRC5.1 0.05 0.01 -0.28 -0.25 -0.23 -0.07 0.03 0.09 0.31
## NRC6.1 0.06 -0.17 -0.11 -0.05 -0.30 -0.06 -0.07 -0.08 0.08
## NRE4.1 NRE5.1 NRE6.1 NRO1.1 NRO2.1 NRO3.1 NRO4.1 NRO5.1 NRO6.1
## NRN1.1 0.03 -0.13 -0.06 0.20 0.13 0.21 -0.07 -0.11 0.09
## NRN2.1 0.10 0.03 -0.10 0.21 0.08 0.22 -0.07 -0.04 0.01
## NRN3.1 -0.17 -0.12 -0.25 0.10 0.07 0.13 -0.17 -0.01 0.04
## NRN4.1 -0.16 -0.03 -0.22 0.07 0.04 -0.01 -0.28 -0.08 -0.10
## NRN5.1 0.02 0.19 0.16 0.35 0.09 0.20 0.14 0.03 0.17
## NRN6.1 -0.12 -0.22 -0.20 0.17 0.02 0.06 -0.09 -0.26 0.02
## NRE1.1 0.25 0.24 0.54 0.08 0.30 0.32 0.25 0.24 0.02
## NRE2.1 0.27 0.25 0.48 0.09 0.16 0.19 0.35 0.04 0.16
## NRE3.1 0.47 0.13 0.40 0.14 0.18 0.27 0.32 0.28 0.19
## NRE4.1 1.00 0.14 0.44 0.03 0.22 0.27 0.33 0.16 0.02
## NRE5.1 0.14 1.00 0.28 0.07 0.21 0.11 0.14 0.25 0.01
## NRE6.1 0.44 0.28 1.00 0.25 0.34 0.36 0.38 0.21 0.10
## NRO1.1 0.03 0.07 0.25 1.00 0.29 0.29 0.19 0.34 0.43
## NRO2.1 0.22 0.21 0.34 0.29 1.00 0.53 0.30 0.46 0.18
## NRO3.1 0.27 0.11 0.36 0.29 0.53 1.00 0.28 0.27 0.16
## NRO4.1 0.33 0.14 0.38 0.19 0.30 0.28 1.00 0.26 0.48
## NRO5.1 0.16 0.25 0.21 0.34 0.46 0.27 0.26 1.00 0.29
## NRO6.1 0.02 0.01 0.10 0.43 0.18 0.16 0.48 0.29 1.00
## NRA1.1 0.17 -0.09 0.29 0.08 0.00 0.00 0.32 0.10 0.26
## NRA2.1 0.02 -0.36 0.12 0.16 0.04 0.06 0.26 -0.08 0.28
## NRA3.1 0.27 -0.03 0.27 0.12 0.17 0.26 0.25 0.09 0.17
## NRA4.1 -0.02 -0.23 0.17 0.12 0.04 -0.04 0.37 0.04 0.34
## NRA5.1 -0.19 -0.31 -0.18 -0.03 -0.02 -0.04 0.08 -0.22 0.23
## NRA6.1 0.03 -0.04 0.17 0.15 0.35 0.24 0.12 0.09 0.11
## NRC1.1 0.21 -0.09 0.23 0.07 0.09 0.18 0.34 0.23 0.31
## NRC2.1 0.25 -0.12 0.17 -0.02 0.03 0.12 0.24 -0.08 0.18
## NRC3.1 0.31 -0.23 0.13 -0.21 0.05 0.08 0.09 -0.04 -0.09
## NRC4.1 0.52 -0.04 0.32 -0.02 0.14 0.23 0.21 0.18 0.06
## NRC5.1 0.36 -0.16 0.18 -0.05 0.02 0.11 0.29 -0.01 0.13
## NRC6.1 0.03 -0.29 -0.02 -0.14 -0.08 0.03 -0.02 -0.06 0.11
## NRA1.1 NRA2.1 NRA3.1 NRA4.1 NRA5.1 NRA6.1 NRC1.1 NRC2.1 NRC3.1
## NRN1.1 -0.17 0.07 0.06 0.10 0.21 0.06 -0.11 0.12 -0.02
## NRN2.1 -0.31 -0.07 -0.21 -0.39 -0.01 -0.09 -0.22 0.13 -0.18
## NRN3.1 -0.22 -0.01 -0.02 0.04 0.26 0.15 -0.35 -0.12 -0.19
## NRN4.1 -0.30 -0.11 -0.17 0.01 0.21 0.02 -0.40 -0.08 -0.19
## NRN5.1 -0.15 -0.02 -0.07 -0.02 -0.03 0.02 -0.16 0.06 -0.25
## NRN6.1 -0.15 0.24 -0.06 0.14 0.42 -0.01 -0.35 0.17 -0.11
## NRE1.1 0.35 0.03 0.45 0.09 -0.15 0.22 0.17 -0.10 0.18
## NRE2.1 0.26 0.11 0.23 0.18 -0.03 0.05 0.08 0.14 0.11
## NRE3.1 0.32 0.10 0.23 -0.02 -0.28 0.03 0.45 0.27 0.23
## NRE4.1 0.17 0.02 0.27 -0.02 -0.19 0.03 0.21 0.25 0.31
## NRE5.1 -0.09 -0.36 -0.03 -0.23 -0.31 -0.04 -0.09 -0.12 -0.23
## NRE6.1 0.29 0.12 0.27 0.17 -0.18 0.17 0.23 0.17 0.13
## NRO1.1 0.08 0.16 0.12 0.12 -0.03 0.15 0.07 -0.02 -0.21
## NRO2.1 0.00 0.04 0.17 0.04 -0.02 0.35 0.09 0.03 0.05
## NRO3.1 0.00 0.06 0.26 -0.04 -0.04 0.24 0.18 0.12 0.08
## NRO4.1 0.32 0.26 0.25 0.37 0.08 0.12 0.34 0.24 0.09
## NRO5.1 0.10 -0.08 0.09 0.04 -0.22 0.09 0.23 -0.08 -0.04
## NRO6.1 0.26 0.28 0.17 0.34 0.23 0.11 0.31 0.18 -0.09
## NRA1.1 1.00 0.38 0.35 0.40 0.13 0.26 0.27 0.15 0.21
## NRA2.1 0.38 1.00 0.41 0.51 0.39 0.13 0.25 0.36 0.26
## NRA3.1 0.35 0.41 1.00 0.32 0.24 0.28 0.34 0.22 0.34
## NRA4.1 0.40 0.51 0.32 1.00 0.37 0.21 0.28 0.27 0.18
## NRA5.1 0.13 0.39 0.24 0.37 1.00 0.22 -0.05 0.19 0.03
## NRA6.1 0.26 0.13 0.28 0.21 0.22 1.00 0.08 -0.04 0.04
## NRC1.1 0.27 0.25 0.34 0.28 -0.05 0.08 1.00 0.42 0.41
## NRC2.1 0.15 0.36 0.22 0.27 0.19 -0.04 0.42 1.00 0.27
## NRC3.1 0.21 0.26 0.34 0.18 0.03 0.04 0.41 0.27 1.00
## NRC4.1 0.14 0.16 0.25 0.15 -0.16 0.00 0.48 0.33 0.42
## NRC5.1 0.22 0.34 0.35 0.21 0.05 -0.03 0.58 0.61 0.47
## NRC6.1 0.08 0.22 0.16 0.32 0.12 0.00 0.48 0.42 0.35
## NRC4.1 NRC5.1 NRC6.1
## NRN1.1 0.01 0.05 0.06
## NRN2.1 0.02 0.01 -0.17
## NRN3.1 -0.20 -0.28 -0.11
## NRN4.1 -0.17 -0.25 -0.05
## NRN5.1 -0.06 -0.23 -0.30
## NRN6.1 -0.19 -0.07 -0.06
## NRE1.1 0.24 0.03 -0.07
## NRE2.1 0.09 0.09 -0.08
## NRE3.1 0.44 0.31 0.08
## NRE4.1 0.52 0.36 0.03
## NRE5.1 -0.04 -0.16 -0.29
## NRE6.1 0.32 0.18 -0.02
## NRO1.1 -0.02 -0.05 -0.14
## NRO2.1 0.14 0.02 -0.08
## NRO3.1 0.23 0.11 0.03
## NRO4.1 0.21 0.29 -0.02
## NRO5.1 0.18 -0.01 -0.06
## NRO6.1 0.06 0.13 0.11
## NRA1.1 0.14 0.22 0.08
## NRA2.1 0.16 0.34 0.22
## NRA3.1 0.25 0.35 0.16
## NRA4.1 0.15 0.21 0.32
## NRA5.1 -0.16 0.05 0.12
## NRA6.1 0.00 -0.03 0.00
## NRC1.1 0.48 0.58 0.48
## NRC2.1 0.33 0.61 0.42
## NRC3.1 0.42 0.47 0.35
## NRC4.1 1.00 0.52 0.32
## NRC5.1 0.52 1.00 0.38
## NRC6.1 0.32 0.38 1.00# HAM-D participants
corr.test(neo.depr.hd[,c(1:30)], method = "spearman")$r %>% round(2)## NRN1.1 NRN2.1 NRN3.1 NRN4.1 NRN5.1 NRN6.1 NRE1.1 NRE2.1 NRE3.1
## NRN1.1 1.00 0.44 0.47 0.55 0.31 0.56 -0.08 -0.09 -0.12
## NRN2.1 0.44 1.00 0.23 0.33 0.36 0.46 -0.18 -0.06 0.03
## NRN3.1 0.47 0.23 1.00 0.61 0.29 0.51 -0.14 -0.20 -0.29
## NRN4.1 0.55 0.33 0.61 1.00 0.31 0.51 -0.21 -0.26 -0.41
## NRN5.1 0.31 0.36 0.29 0.31 1.00 0.27 0.01 -0.02 0.03
## NRN6.1 0.56 0.46 0.51 0.51 0.27 1.00 -0.21 -0.08 -0.28
## NRE1.1 -0.08 -0.18 -0.14 -0.21 0.01 -0.21 1.00 0.53 0.37
## NRE2.1 -0.09 -0.06 -0.20 -0.26 -0.02 -0.08 0.53 1.00 0.39
## NRE3.1 -0.12 0.03 -0.29 -0.41 0.03 -0.28 0.37 0.39 1.00
## NRE4.1 0.01 0.11 -0.22 -0.13 0.03 -0.14 0.31 0.30 0.48
## NRE5.1 -0.19 0.04 -0.11 -0.08 0.15 -0.24 0.22 0.29 0.19
## NRE6.1 -0.09 -0.10 -0.27 -0.24 0.07 -0.21 0.52 0.47 0.41
## NRO1.1 0.21 0.21 0.16 0.16 0.35 0.13 0.07 0.05 0.14
## NRO2.1 0.14 0.07 0.09 0.06 0.10 -0.05 0.32 0.12 0.15
## NRO3.1 0.25 0.22 0.16 0.05 0.22 0.09 0.37 0.16 0.25
## NRO4.1 -0.04 -0.09 -0.14 -0.21 0.11 -0.07 0.28 0.34 0.28
## NRO5.1 -0.07 -0.08 -0.04 -0.06 0.01 -0.28 0.26 0.06 0.27
## NRO6.1 0.12 0.01 0.11 -0.01 0.18 0.05 0.06 0.09 0.16
## NRA1.1 -0.17 -0.38 -0.23 -0.28 -0.12 -0.19 0.35 0.21 0.25
## NRA2.1 0.13 -0.05 0.05 -0.02 0.00 0.24 0.10 0.08 0.07
## NRA3.1 0.09 -0.19 0.00 -0.12 -0.05 -0.05 0.50 0.22 0.21
## NRA4.1 0.14 -0.38 0.08 0.04 0.00 0.16 0.14 0.08 -0.07
## NRA5.1 0.29 0.05 0.33 0.30 0.07 0.45 -0.11 -0.10 -0.31
## NRA6.1 0.09 -0.12 0.09 0.04 -0.01 -0.04 0.21 0.05 0.00
## NRC1.1 -0.10 -0.17 -0.35 -0.33 -0.19 -0.41 0.24 0.16 0.41
## NRC2.1 0.17 0.15 -0.08 -0.02 0.04 0.09 0.01 0.09 0.22
## NRC3.1 0.08 -0.10 -0.14 -0.13 -0.17 -0.11 0.21 0.09 0.16
## NRC4.1 0.06 0.07 -0.21 -0.14 -0.05 -0.21 0.28 0.09 0.43
## NRC5.1 0.07 0.04 -0.25 -0.20 -0.17 -0.13 0.11 0.12 0.31
## NRC6.1 0.06 -0.14 -0.08 -0.07 -0.36 -0.04 0.00 -0.03 0.03
## NRE4.1 NRE5.1 NRE6.1 NRO1.1 NRO2.1 NRO3.1 NRO4.1 NRO5.1 NRO6.1
## NRN1.1 0.01 -0.19 -0.09 0.21 0.14 0.25 -0.04 -0.07 0.12
## NRN2.1 0.11 0.04 -0.10 0.21 0.07 0.22 -0.09 -0.08 0.01
## NRN3.1 -0.22 -0.11 -0.27 0.16 0.09 0.16 -0.14 -0.04 0.11
## NRN4.1 -0.13 -0.08 -0.24 0.16 0.06 0.05 -0.21 -0.06 -0.01
## NRN5.1 0.03 0.15 0.07 0.35 0.10 0.22 0.11 0.01 0.18
## NRN6.1 -0.14 -0.24 -0.21 0.13 -0.05 0.09 -0.07 -0.28 0.05
## NRE1.1 0.31 0.22 0.52 0.07 0.32 0.37 0.28 0.26 0.06
## NRE2.1 0.30 0.29 0.47 0.05 0.12 0.16 0.34 0.06 0.09
## NRE3.1 0.48 0.19 0.41 0.14 0.15 0.25 0.28 0.27 0.16
## NRE4.1 1.00 0.15 0.47 0.03 0.20 0.26 0.29 0.17 0.05
## NRE5.1 0.15 1.00 0.32 0.13 0.24 0.14 0.09 0.28 0.02
## NRE6.1 0.47 0.32 1.00 0.27 0.33 0.40 0.31 0.21 0.09
## NRO1.1 0.03 0.13 0.27 1.00 0.34 0.35 0.20 0.33 0.40
## NRO2.1 0.20 0.24 0.33 0.34 1.00 0.54 0.27 0.49 0.22
## NRO3.1 0.26 0.14 0.40 0.35 0.54 1.00 0.27 0.30 0.22
## NRO4.1 0.29 0.09 0.31 0.20 0.27 0.27 1.00 0.21 0.47
## NRO5.1 0.17 0.28 0.21 0.33 0.49 0.30 0.21 1.00 0.25
## NRO6.1 0.05 0.02 0.09 0.40 0.22 0.22 0.47 0.25 1.00
## NRA1.1 0.15 -0.09 0.22 -0.01 -0.01 0.03 0.31 0.07 0.23
## NRA2.1 0.02 -0.33 0.01 -0.01 0.00 0.02 0.24 -0.10 0.25
## NRA3.1 0.19 -0.01 0.20 0.04 0.20 0.28 0.23 0.12 0.17
## NRA4.1 -0.06 -0.30 0.07 0.04 0.02 -0.02 0.32 0.00 0.33
## NRA5.1 -0.22 -0.30 -0.24 -0.07 -0.06 -0.05 0.04 -0.26 0.17
## NRA6.1 -0.01 -0.01 0.08 0.12 0.29 0.22 0.18 0.14 0.17
## NRC1.1 0.28 -0.01 0.26 0.03 0.16 0.18 0.31 0.24 0.24
## NRC2.1 0.29 -0.05 0.18 -0.05 0.09 0.16 0.15 -0.04 0.11
## NRC3.1 0.25 -0.13 0.06 -0.20 0.04 0.11 0.12 0.02 -0.03
## NRC4.1 0.55 0.02 0.32 0.00 0.17 0.25 0.16 0.22 0.07
## NRC5.1 0.42 -0.09 0.19 -0.09 0.06 0.12 0.25 0.03 0.09
## NRC6.1 -0.03 -0.25 -0.03 -0.13 -0.02 0.06 0.04 -0.03 0.07
## NRA1.1 NRA2.1 NRA3.1 NRA4.1 NRA5.1 NRA6.1 NRC1.1 NRC2.1 NRC3.1
## NRN1.1 -0.17 0.13 0.09 0.14 0.29 0.09 -0.10 0.17 0.08
## NRN2.1 -0.38 -0.05 -0.19 -0.38 0.05 -0.12 -0.17 0.15 -0.10
## NRN3.1 -0.23 0.05 0.00 0.08 0.33 0.09 -0.35 -0.08 -0.14
## NRN4.1 -0.28 -0.02 -0.12 0.04 0.30 0.04 -0.33 -0.02 -0.13
## NRN5.1 -0.12 0.00 -0.05 0.00 0.07 -0.01 -0.19 0.04 -0.17
## NRN6.1 -0.19 0.24 -0.05 0.16 0.45 -0.04 -0.41 0.09 -0.11
## NRE1.1 0.35 0.10 0.50 0.14 -0.11 0.21 0.24 0.01 0.21
## NRE2.1 0.21 0.08 0.22 0.08 -0.10 0.05 0.16 0.09 0.09
## NRE3.1 0.25 0.07 0.21 -0.07 -0.31 0.00 0.41 0.22 0.16
## NRE4.1 0.15 0.02 0.19 -0.06 -0.22 -0.01 0.28 0.29 0.25
## NRE5.1 -0.09 -0.33 -0.01 -0.30 -0.30 -0.01 -0.01 -0.05 -0.13
## NRE6.1 0.22 0.01 0.20 0.07 -0.24 0.08 0.26 0.18 0.06
## NRO1.1 -0.01 -0.01 0.04 0.04 -0.07 0.12 0.03 -0.05 -0.20
## NRO2.1 -0.01 0.00 0.20 0.02 -0.06 0.29 0.16 0.09 0.04
## NRO3.1 0.03 0.02 0.28 -0.02 -0.05 0.22 0.18 0.16 0.11
## NRO4.1 0.31 0.24 0.23 0.32 0.04 0.18 0.31 0.15 0.12
## NRO5.1 0.07 -0.10 0.12 0.00 -0.26 0.14 0.24 -0.04 0.02
## NRO6.1 0.23 0.25 0.17 0.33 0.17 0.17 0.24 0.11 -0.03
## NRA1.1 1.00 0.35 0.33 0.40 0.06 0.22 0.28 0.11 0.20
## NRA2.1 0.35 1.00 0.43 0.48 0.41 0.17 0.18 0.28 0.31
## NRA3.1 0.33 0.43 1.00 0.34 0.24 0.35 0.35 0.23 0.38
## NRA4.1 0.40 0.48 0.34 1.00 0.37 0.24 0.17 0.17 0.18
## NRA5.1 0.06 0.41 0.24 0.37 1.00 0.21 -0.17 0.14 0.08
## NRA6.1 0.22 0.17 0.35 0.24 0.21 1.00 0.11 -0.03 0.06
## NRC1.1 0.28 0.18 0.35 0.17 -0.17 0.11 1.00 0.43 0.44
## NRC2.1 0.11 0.28 0.23 0.17 0.14 -0.03 0.43 1.00 0.32
## NRC3.1 0.20 0.31 0.38 0.18 0.08 0.06 0.44 0.32 1.00
## NRC4.1 0.12 0.20 0.28 0.08 -0.19 -0.02 0.51 0.40 0.45
## NRC5.1 0.19 0.31 0.28 0.12 0.00 0.01 0.60 0.62 0.48
## NRC6.1 0.06 0.23 0.21 0.27 0.06 0.06 0.44 0.38 0.34
## NRC4.1 NRC5.1 NRC6.1
## NRN1.1 0.06 0.07 0.06
## NRN2.1 0.07 0.04 -0.14
## NRN3.1 -0.21 -0.25 -0.08
## NRN4.1 -0.14 -0.20 -0.07
## NRN5.1 -0.05 -0.17 -0.36
## NRN6.1 -0.21 -0.13 -0.04
## NRE1.1 0.28 0.11 0.00
## NRE2.1 0.09 0.12 -0.03
## NRE3.1 0.43 0.31 0.03
## NRE4.1 0.55 0.42 -0.03
## NRE5.1 0.02 -0.09 -0.25
## NRE6.1 0.32 0.19 -0.03
## NRO1.1 0.00 -0.09 -0.13
## NRO2.1 0.17 0.06 -0.02
## NRO3.1 0.25 0.12 0.06
## NRO4.1 0.16 0.25 0.04
## NRO5.1 0.22 0.03 -0.03
## NRO6.1 0.07 0.09 0.07
## NRA1.1 0.12 0.19 0.06
## NRA2.1 0.20 0.31 0.23
## NRA3.1 0.28 0.28 0.21
## NRA4.1 0.08 0.12 0.27
## NRA5.1 -0.19 0.00 0.06
## NRA6.1 -0.02 0.01 0.06
## NRC1.1 0.51 0.60 0.44
## NRC2.1 0.40 0.62 0.38
## NRC3.1 0.45 0.48 0.34
## NRC4.1 1.00 0.57 0.27
## NRC5.1 0.57 1.00 0.37
## NRC6.1 0.27 0.37 1.004.7 Treatment effects
# BDI-II scores
lm(BDI2.2 ~ BDI2.1 + txgrp, data = neo.depr.bdi) %>% summ(conf = T, robust = T, digits = 3)| Observations | 208 |
| Dependent variable | BDI2.2 |
| Type | OLS linear regression |
| F(2,205) | 18.862 |
| R² | 0.155 |
| Adj. R² | 0.147 |
| Est. | 2.5% | 97.5% | t val. | p | |
|---|---|---|---|---|---|
| (Intercept) | 0.003 | -5.601 | 5.607 | 0.001 | 0.999 |
| BDI2.1 | 0.514 | 0.334 | 0.694 | 5.618 | 0.000 |
| txgrp | 1.206 | -1.619 | 4.030 | 0.841 | 0.401 |
| Standard errors: Robust, type = HC3 |
# HAM-D scores
lm(hamd2 ~ hamd1 + txgrp, data = neo.depr.hd) %>% summ(conf = T, robust = T, digits = 3)| Observations | 325 |
| Dependent variable | hamd2 |
| Type | OLS linear regression |
| F(2,322) | 21.687 |
| R² | 0.119 |
| Adj. R² | 0.113 |
| Est. | 2.5% | 97.5% | t val. | p | |
|---|---|---|---|---|---|
| (Intercept) | -0.988 | -3.978 | 2.001 | -0.651 | 0.516 |
| hamd1 | 0.485 | 0.321 | 0.649 | 5.804 | 0.000 |
| txgrp | 0.585 | -1.009 | 2.180 | 0.722 | 0.471 |
| Standard errors: Robust, type = HC3 |
5 Overall treatment response
5.1 Functions
## Best model function
best.model = function(model) {
best = which(rownames(model$results) == rownames(model$bestTune))
best.result = model$results[best,]
rownames(best.result) = NULL
best.result # Gives you the following from the best performing model: hyperparameters, RMSE (and its SD),
# R2 (and its SD), and MAE (and its SD)
}
## Cross-validation settings
ctrl.overall = trainControl(method = "repeatedcv", # Repeated k-fold CV
number = 5, # 5-fold CV
repeats = 5, # Repeated 5 times
savePredictions = T,
verbose = F)
## Standardizing variables function
standardizing = function(data = NULL, var.info = NULL){
processed.data = list()
var.names = var.info[, 1]
var.types = var.info[, 2]
data.cont = subset(data, select = var.names[var.types=="numeric"])
data.binary = subset(data, select = var.names[var.types=="binary"])
zscoring.info = preProcess(data.cont, method = c("center", "scale")) # Getting z-scores for IVs
numeric.z = predict(zscoring.info, data.cont)
data.zscored = data
data.zscored[, var.names[var.types=="binary"]] = data.binary
data.zscored[, var.names[var.types=="numeric"]] = numeric.z
return(data.zscored)
}
bdi.info = matrix(c(
"NRN1.1", "numeric",
"NRN2.1", "numeric",
"NRN3.1", "numeric",
"NRN4.1", "numeric",
"NRN5.1", "numeric",
"NRN6.1", "numeric",
"NRE1.1", "numeric",
"NRE2.1", "numeric",
"NRE3.1", "numeric",
"NRE4.1", "numeric",
"NRE5.1", "numeric",
"NRE6.1", "numeric",
"NRO1.1", "numeric",
"NRO2.1", "numeric",
"NRO3.1", "numeric",
"NRO4.1", "numeric",
"NRO5.1", "numeric",
"NRO6.1", "numeric",
"NRA1.1", "numeric",
"NRA2.1", "numeric",
"NRA3.1", "numeric",
"NRA4.1", "numeric",
"NRA5.1", "numeric",
"NRA6.1", "numeric",
"NRC1.1", "numeric",
"NRC2.1", "numeric",
"NRC3.1", "numeric",
"NRC4.1", "numeric",
"NRC5.1", "numeric",
"NRC6.1", "numeric",
"BDI2.1", "numeric",
"sex", "binary",
"age", "numeric"
), ncol = 2, byrow = T)
hd.info = matrix(c(
"NRN1.1", "numeric",
"NRN2.1", "numeric",
"NRN3.1", "numeric",
"NRN4.1", "numeric",
"NRN5.1", "numeric",
"NRN6.1", "numeric",
"NRE1.1", "numeric",
"NRE2.1", "numeric",
"NRE3.1", "numeric",
"NRE4.1", "numeric",
"NRE5.1", "numeric",
"NRE6.1", "numeric",
"NRO1.1", "numeric",
"NRO2.1", "numeric",
"NRO3.1", "numeric",
"NRO4.1", "numeric",
"NRO5.1", "numeric",
"NRO6.1", "numeric",
"NRA1.1", "numeric",
"NRA2.1", "numeric",
"NRA3.1", "numeric",
"NRA4.1", "numeric",
"NRA5.1", "numeric",
"NRA6.1", "numeric",
"NRC1.1", "numeric",
"NRC2.1", "numeric",
"NRC3.1", "numeric",
"NRC4.1", "numeric",
"NRC5.1", "numeric",
"NRC6.1", "numeric",
"hamd1", "numeric",
"sex", "binary",
"age", "numeric"
), ncol = 2, byrow = T)
iv.bdi.names = bdi.info[, 1]
iv.hd.names = hd.info[, 1]
###### Elastic net regression function #####
elastic.net = function(data, zscored.ivs, outcome){
eGrid = expand.grid(.alpha = seq(0, 1, by = .25), .lambda = seq(0, 2, by = .05)) # Parameter space for possible hyperparameters
set.seed(2020)
model = caret::train(y ~ .,
data = data,
method = "glmnet",
metric = "RMSE", # Pick the model with the lowest RMSE
tuneGrid = eGrid,
trControl = ctrl.overall)
model.stats = getTrainPerf(model)
print(model.stats)
print(best.model(model))
print(model$bestTune)
final.model = glmnet::glmnet(data.matrix(zscored.ivs), as.matrix(outcome), alpha = model$bestTune["alpha"],
lambda = model$bestTune["lambda"]) # Fitting the ENR model with best hyperparameters
print(coef(final.model))
return(final.model)
}5.2 BDI-II scores
## Splitting data into training and test sets
set.seed(2020)
bdi.index = createDataPartition(neo.depr.bdi$BDI2.2, p = .7, list = F) # 70/30 split
bdi.overall.train = neo.depr.bdi[bdi.index, ]
bdi.overall.test = neo.depr.bdi[-bdi.index, ]
nrow(bdi.overall.train)## [1] 147nrow(bdi.overall.test)## [1] 615.2.1 Ordinary least squares
#### Pre-tx BDI-II only
bdi.overall.lm1 = lm(BDI2.2 ~ BDI2.1, data = bdi.overall.train)
summ(bdi.overall.lm1)| Observations | 147 |
| Dependent variable | BDI2.2 |
| Type | OLS linear regression |
| F(1,145) | 31.70 |
| R² | 0.18 |
| Adj. R² | 0.17 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -1.13 | 3.18 | -0.35 | 0.72 |
| BDI2.1 | 0.57 | 0.10 | 5.63 | 0.00 |
| Standard errors: OLS |
# Test sample
pred.bdi.ov.lm1.test = predict(bdi.overall.lm1, bdi.overall.test)
postResample(pred.bdi.ov.lm1.test, bdi.overall.test$BDI2.2) # R2 = 10.63%, MAE = 8.95, RMSE = 11.24## RMSE Rsquared MAE
## 11.2391577 0.1063268 8.9479098#### Model 1: Pre-tx BDI-II and demographics
bdi.overall.lm2 = lm(BDI2.2 ~ BDI2.1 + age + sex, data = bdi.overall.train)
summ(bdi.overall.lm2)| Observations | 147 |
| Dependent variable | BDI2.2 |
| Type | OLS linear regression |
| F(3,143) | 10.61 |
| R² | 0.18 |
| Adj. R² | 0.16 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -2.38 | 4.52 | -0.53 | 0.60 |
| BDI2.1 | 0.56 | 0.10 | 5.42 | 0.00 |
| age | 0.02 | 0.07 | 0.29 | 0.77 |
| sex1 | 1.09 | 1.74 | 0.63 | 0.53 |
| Standard errors: OLS |
# Test sample
pred.bdi.ov.lm2.test = predict(bdi.overall.lm2, bdi.overall.test)
postResample(pred.bdi.ov.lm2.test, bdi.overall.test$BDI2.2) # R2 = 11.43%, MAE = 8.86, RMSE = 11.17## RMSE Rsquared MAE
## 11.1716562 0.1143248 8.8581140#### Model 2: FFM facets only
bdi.overall.train.facets = bdi.overall.train[,c(1:30, 32)]
bdi.overall.lm3 = lm(BDI2.2 ~ ., data = bdi.overall.train.facets)
summ(bdi.overall.lm3)| Observations | 147 |
| Dependent variable | BDI2.2 |
| Type | OLS linear regression |
| F(30,116) | 2.28 |
| R² | 0.37 |
| Adj. R² | 0.21 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -21.96 | 12.79 | -1.72 | 0.09 |
| NRN1.1 | 0.42 | 0.23 | 1.85 | 0.07 |
| NRN2.1 | -0.11 | 0.25 | -0.44 | 0.66 |
| NRN3.1 | 0.55 | 0.29 | 1.89 | 0.06 |
| NRN4.1 | 0.19 | 0.26 | 0.71 | 0.48 |
| NRN5.1 | 0.02 | 0.21 | 0.11 | 0.91 |
| NRN6.1 | -0.25 | 0.31 | -0.78 | 0.44 |
| NRE1.1 | -0.63 | 0.29 | -2.16 | 0.03 |
| NRE2.1 | 0.56 | 0.22 | 2.59 | 0.01 |
| NRE3.1 | 0.33 | 0.23 | 1.43 | 0.16 |
| NRE4.1 | -0.32 | 0.26 | -1.24 | 0.22 |
| NRE5.1 | 0.21 | 0.21 | 0.98 | 0.33 |
| NRE6.1 | -0.14 | 0.24 | -0.57 | 0.57 |
| NRO1.1 | -0.04 | 0.22 | -0.18 | 0.85 |
| NRO2.1 | -0.09 | 0.19 | -0.48 | 0.64 |
| NRO3.1 | 0.03 | 0.26 | 0.13 | 0.90 |
| NRO4.1 | -0.04 | 0.26 | -0.15 | 0.88 |
| NRO5.1 | 0.03 | 0.19 | 0.16 | 0.87 |
| NRO6.1 | -0.25 | 0.26 | -0.95 | 0.34 |
| NRA1.1 | -0.17 | 0.22 | -0.75 | 0.45 |
| NRA2.1 | 0.26 | 0.24 | 1.07 | 0.29 |
| NRA3.1 | -0.01 | 0.32 | -0.03 | 0.97 |
| NRA4.1 | -0.16 | 0.29 | -0.56 | 0.57 |
| NRA5.1 | 0.75 | 0.23 | 3.23 | 0.00 |
| NRA6.1 | 0.03 | 0.27 | 0.10 | 0.92 |
| NRC1.1 | 1.03 | 0.33 | 3.14 | 0.00 |
| NRC2.1 | 0.10 | 0.22 | 0.44 | 0.66 |
| NRC3.1 | 0.09 | 0.27 | 0.31 | 0.75 |
| NRC4.1 | 0.33 | 0.22 | 1.50 | 0.14 |
| NRC5.1 | -0.43 | 0.25 | -1.77 | 0.08 |
| NRC6.1 | -0.39 | 0.23 | -1.69 | 0.09 |
| Standard errors: OLS |
# Test sample
bdi.overall.test.facets = bdi.overall.test[,c(1:30, 32)]
pred.bdi.ov.lm3.test = predict(bdi.overall.lm3, bdi.overall.test.facets)
postResample(pred.bdi.ov.lm3.test, bdi.overall.test.facets$BDI2.2) # R2 = 1.20%, MAE = 9.58, RMSE = 12.85## RMSE Rsquared MAE
## 12.86223438 0.01606031 10.12509186#### Model 3: Pre-tx BDI-II and FFM facets
bdi.overall.train.facets2 = bdi.overall.train[,c(1:32)]
bdi.overall.lm4 = lm(BDI2.2 ~ ., data = bdi.overall.train.facets2)
summ(bdi.overall.lm4)| Observations | 147 |
| Dependent variable | BDI2.2 |
| Type | OLS linear regression |
| F(31,115) | 2.85 |
| R² | 0.43 |
| Adj. R² | 0.28 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -26.22 | 12.25 | -2.14 | 0.03 |
| NRN1.1 | 0.30 | 0.22 | 1.40 | 0.17 |
| NRN2.1 | -0.12 | 0.23 | -0.51 | 0.61 |
| NRN3.1 | 0.39 | 0.28 | 1.38 | 0.17 |
| NRN4.1 | 0.10 | 0.25 | 0.41 | 0.69 |
| NRN5.1 | -0.07 | 0.20 | -0.37 | 0.71 |
| NRN6.1 | -0.26 | 0.30 | -0.88 | 0.38 |
| NRE1.1 | -0.58 | 0.28 | -2.09 | 0.04 |
| NRE2.1 | 0.53 | 0.21 | 2.60 | 0.01 |
| NRE3.1 | 0.26 | 0.22 | 1.18 | 0.24 |
| NRE4.1 | -0.34 | 0.25 | -1.35 | 0.18 |
| NRE5.1 | 0.20 | 0.20 | 0.99 | 0.32 |
| NRE6.1 | 0.00 | 0.23 | 0.02 | 0.99 |
| NRO1.1 | 0.08 | 0.22 | 0.35 | 0.73 |
| NRO2.1 | -0.04 | 0.18 | -0.24 | 0.81 |
| NRO3.1 | -0.04 | 0.25 | -0.17 | 0.87 |
| NRO4.1 | -0.03 | 0.25 | -0.14 | 0.89 |
| NRO5.1 | -0.04 | 0.19 | -0.24 | 0.81 |
| NRO6.1 | -0.19 | 0.25 | -0.78 | 0.44 |
| NRA1.1 | -0.17 | 0.21 | -0.79 | 0.43 |
| NRA2.1 | 0.16 | 0.23 | 0.68 | 0.49 |
| NRA3.1 | -0.10 | 0.31 | -0.34 | 0.74 |
| NRA4.1 | -0.10 | 0.28 | -0.37 | 0.71 |
| NRA5.1 | 0.63 | 0.22 | 2.81 | 0.01 |
| NRA6.1 | 0.07 | 0.26 | 0.25 | 0.80 |
| NRC1.1 | 0.84 | 0.32 | 2.68 | 0.01 |
| NRC2.1 | 0.11 | 0.21 | 0.53 | 0.60 |
| NRC3.1 | 0.09 | 0.26 | 0.36 | 0.72 |
| NRC4.1 | 0.40 | 0.21 | 1.88 | 0.06 |
| NRC5.1 | -0.37 | 0.23 | -1.58 | 0.12 |
| NRC6.1 | -0.14 | 0.23 | -0.61 | 0.54 |
| BDI2.1 | 0.45 | 0.13 | 3.58 | 0.00 |
| Standard errors: OLS |
# Test sample
bdi.overall.test.facets2 = bdi.overall.test[,c(1:32)]
pred.bdi.ov.lm4.test = predict(bdi.overall.lm4, bdi.overall.test.facets2)
postResample(pred.bdi.ov.lm4.test, bdi.overall.test.facets2$BDI2.2) # R2 = 8.42%, MAE = 8.90, RMSE = 12.02## RMSE Rsquared MAE
## 12.07438487 0.08208234 9.36171934#### Model 4: Demographics and FFM facets
bdi.overall.train.facets3 = bdi.overall.train[,c(1:30, 35, 36, 32)]
bdi.overall.lm6 = lm(BDI2.2 ~ ., data = bdi.overall.train.facets3)
summ(bdi.overall.lm6)| Observations | 147 |
| Dependent variable | BDI2.2 |
| Type | OLS linear regression |
| F(32,114) | 2.11 |
| R² | 0.37 |
| Adj. R² | 0.20 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -21.39 | 13.47 | -1.59 | 0.12 |
| NRN1.1 | 0.42 | 0.23 | 1.84 | 0.07 |
| NRN2.1 | -0.11 | 0.25 | -0.42 | 0.68 |
| NRN3.1 | 0.55 | 0.30 | 1.87 | 0.06 |
| NRN4.1 | 0.18 | 0.26 | 0.70 | 0.49 |
| NRN5.1 | 0.02 | 0.21 | 0.09 | 0.93 |
| NRN6.1 | -0.24 | 0.32 | -0.76 | 0.45 |
| NRE1.1 | -0.64 | 0.30 | -2.15 | 0.03 |
| NRE2.1 | 0.56 | 0.22 | 2.55 | 0.01 |
| NRE3.1 | 0.34 | 0.24 | 1.41 | 0.16 |
| NRE4.1 | -0.33 | 0.26 | -1.23 | 0.22 |
| NRE5.1 | 0.20 | 0.23 | 0.88 | 0.38 |
| NRE6.1 | -0.13 | 0.25 | -0.53 | 0.59 |
| NRO1.1 | -0.04 | 0.23 | -0.19 | 0.85 |
| NRO2.1 | -0.09 | 0.19 | -0.44 | 0.66 |
| NRO3.1 | 0.02 | 0.27 | 0.09 | 0.93 |
| NRO4.1 | -0.04 | 0.26 | -0.15 | 0.88 |
| NRO5.1 | 0.03 | 0.20 | 0.18 | 0.86 |
| NRO6.1 | -0.25 | 0.27 | -0.94 | 0.35 |
| NRA1.1 | -0.16 | 0.23 | -0.69 | 0.49 |
| NRA2.1 | 0.26 | 0.24 | 1.07 | 0.29 |
| NRA3.1 | -0.01 | 0.32 | -0.03 | 0.98 |
| NRA4.1 | -0.16 | 0.30 | -0.54 | 0.59 |
| NRA5.1 | 0.74 | 0.24 | 3.06 | 0.00 |
| NRA6.1 | 0.02 | 0.28 | 0.07 | 0.94 |
| NRC1.1 | 1.02 | 0.34 | 3.04 | 0.00 |
| NRC2.1 | 0.09 | 0.22 | 0.43 | 0.67 |
| NRC3.1 | 0.09 | 0.28 | 0.33 | 0.74 |
| NRC4.1 | 0.33 | 0.23 | 1.44 | 0.15 |
| NRC5.1 | -0.44 | 0.25 | -1.75 | 0.08 |
| NRC6.1 | -0.39 | 0.23 | -1.67 | 0.10 |
| sex1 | 0.30 | 2.08 | 0.15 | 0.88 |
| age | -0.01 | 0.09 | -0.10 | 0.92 |
| Standard errors: OLS |
# Test sample
bdi.overall.test.facets3 = bdi.overall.test[,c(1:30, 35, 36, 32)]
pred.bdi.ov.lm6.test = predict(bdi.overall.lm6, bdi.overall.test.facets3)
postResample(pred.bdi.ov.lm6.test, bdi.overall.test.facets3$BDI2.2) # R2 = 1.43%, MAE = 9.48, RMSE = 12.76## RMSE Rsquared MAE
## 12.82524834 0.01662966 10.08334905#### Model 5: Every IV
bdi.overall.train = bdi.overall.train[,c(1:32, 35, 36)]
bdi.overall.lm5 = lm(BDI2.2 ~ ., data = bdi.overall.train)
summ(bdi.overall.lm5)| Observations | 147 |
| Dependent variable | BDI2.2 |
| Type | OLS linear regression |
| F(33,113) | 2.64 |
| R² | 0.44 |
| Adj. R² | 0.27 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -27.78 | 12.95 | -2.15 | 0.03 |
| NRN1.1 | 0.30 | 0.22 | 1.35 | 0.18 |
| NRN2.1 | -0.13 | 0.24 | -0.54 | 0.59 |
| NRN3.1 | 0.39 | 0.29 | 1.36 | 0.18 |
| NRN4.1 | 0.10 | 0.25 | 0.41 | 0.69 |
| NRN5.1 | -0.07 | 0.21 | -0.34 | 0.74 |
| NRN6.1 | -0.26 | 0.30 | -0.87 | 0.39 |
| NRE1.1 | -0.57 | 0.28 | -2.02 | 0.05 |
| NRE2.1 | 0.54 | 0.21 | 2.60 | 0.01 |
| NRE3.1 | 0.25 | 0.23 | 1.08 | 0.28 |
| NRE4.1 | -0.33 | 0.25 | -1.30 | 0.20 |
| NRE5.1 | 0.22 | 0.22 | 0.98 | 0.33 |
| NRE6.1 | -0.01 | 0.24 | -0.03 | 0.98 |
| NRO1.1 | 0.09 | 0.23 | 0.40 | 0.69 |
| NRO2.1 | -0.05 | 0.19 | -0.29 | 0.77 |
| NRO3.1 | -0.02 | 0.25 | -0.08 | 0.93 |
| NRO4.1 | -0.04 | 0.25 | -0.15 | 0.88 |
| NRO5.1 | -0.05 | 0.19 | -0.27 | 0.79 |
| NRO6.1 | -0.19 | 0.26 | -0.76 | 0.45 |
| NRA1.1 | -0.19 | 0.22 | -0.87 | 0.39 |
| NRA2.1 | 0.15 | 0.23 | 0.64 | 0.52 |
| NRA3.1 | -0.11 | 0.31 | -0.35 | 0.73 |
| NRA4.1 | -0.11 | 0.28 | -0.40 | 0.69 |
| NRA5.1 | 0.65 | 0.23 | 2.80 | 0.01 |
| NRA6.1 | 0.08 | 0.27 | 0.32 | 0.75 |
| NRC1.1 | 0.85 | 0.32 | 2.64 | 0.01 |
| NRC2.1 | 0.11 | 0.21 | 0.54 | 0.59 |
| NRC3.1 | 0.08 | 0.26 | 0.29 | 0.77 |
| NRC4.1 | 0.42 | 0.22 | 1.91 | 0.06 |
| NRC5.1 | -0.36 | 0.24 | -1.51 | 0.13 |
| NRC6.1 | -0.14 | 0.23 | -0.59 | 0.56 |
| BDI2.1 | 0.46 | 0.13 | 3.57 | 0.00 |
| sex1 | -0.55 | 1.99 | -0.28 | 0.78 |
| age | 0.03 | 0.09 | 0.30 | 0.77 |
| Standard errors: OLS |
# Test sample
bdi.overall.test = bdi.overall.test[,c(1:32, 35, 36)]
pred.bdi.ov.lm5.test = predict(bdi.overall.lm5, bdi.overall.test)
postResample(pred.bdi.ov.lm5.test, bdi.overall.test$BDI2.2) # R2 = 8.36%, MAE = 8.96, RMSE = 12.08## RMSE Rsquared MAE
## 12.16110051 0.08038503 9.433751655.2.2 Elastic net regression
#### Standardizing IVs
# Training set
iv.bdi.process = subset(bdi.overall.train, select = iv.bdi.names)
iv.bdi.processed = standardizing(data = iv.bdi.process, var.info = bdi.info)
bdi.overall.train.z = iv.bdi.processed
bdi.overall.train.z$y = bdi.overall.train$BDI2.2
bdi.overall.train.outcome = select(bdi.overall.train, BDI2.2)
# Test set
iv.bdi.process.test = subset(bdi.overall.test, select = iv.bdi.names)
iv.bdi.processed.test = standardizing(data = iv.bdi.process.test, var.info = bdi.info)
bdi.overall.test.z = iv.bdi.processed.test
bdi.overall.test.z$y = bdi.overall.test$BDI2.2
bdi.overall.test.outcome = select(bdi.overall.test, BDI2.2)
#### Model 1: Pre-tx BDI-II and demographics only
iv.bdi.processed1 = iv.bdi.processed[,c(31:33)]
bdi.overall.train.z1 = bdi.overall.train.z[,c(31:34)]
bdi.overall.enr1 = elastic.net(data = bdi.overall.train.z1, zscored.ivs = iv.bdi.processed1, outcome = bdi.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 10.09655 0.192745 8.283202 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.75 10.09655 0.192745 8.283202 0.9195208 0.1160524 0.7474014
## alpha lambda
## 180 1 0.75
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.146058
## BDI2.1 3.927977
## sex .
## age .# Test sample
bdi.overall.test.enr1 = bdi.overall.test.z[,c(31:33)]
pred.bdi.ov.enr1.test = predict.glmnet(bdi.overall.enr1, newx = data.matrix(bdi.overall.test.enr1))
postResample(pred.bdi.ov.enr1.test, bdi.overall.test$BDI2.2) # R2 = 10.63%, MAE = 8.80, RMSE = 11.14## RMSE Rsquared MAE
## 11.1441901 0.1063268 8.7970576#### Model 2: FFM facets only
iv.bdi.processed2 = iv.bdi.processed[,c(1:30)]
bdi.overall.train.z2 = bdi.overall.train.z[,c(1:30, 34)]
bdi.overall.enr2 = elastic.net(data = bdi.overall.train.z2, zscored.ivs = iv.bdi.processed2, outcome = bdi.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 10.4112 0.1343076 8.44691 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.5 1.2 10.4112 0.1343076 8.44691 0.9757249 0.1078292 0.7913572
## alpha lambda
## 107 0.5 1.2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.14605822
## NRN1.1 1.39352466
## NRN2.1 .
## NRN3.1 1.84649413
## NRN4.1 0.34732430
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.85805380
## NRE2.1 0.84820320
## NRE3.1 0.92611095
## NRE4.1 -0.09747707
## NRE5.1 0.17546892
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.48402044
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.96374127
## NRA6.1 .
## NRC1.1 2.05620430
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .# Test sample
bdi.overall.test.enr2 = bdi.overall.test.z[,c(1:30)]
pred.bdi.ov.enr2.test = predict.glmnet(bdi.overall.enr2, newx = data.matrix(bdi.overall.test.enr2))
postResample(pred.bdi.ov.enr2.test, bdi.overall.test$BDI2.2) # R2 = 2.17%, MAE = 9.37, RMSE = 11.85## RMSE Rsquared MAE
## 11.85178638 0.02658424 9.54770878#### Model 3: Pre-tx BDI-II and FFM facets
iv.bdi.processed3 = iv.bdi.processed[,c(1:31)]
bdi.overall.train.z3 = bdi.overall.train.z[,c(1:31, 34)]
bdi.overall.enr3 = elastic.net(data = bdi.overall.train.z3, zscored.ivs = iv.bdi.processed3, outcome = bdi.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 9.843801 0.2202247 8.020181 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.6 9.843801 0.2202247 8.020181 0.9595405 0.09697202 0.7527895
## alpha lambda
## 177 1 0.6
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.1460582
## NRN1.1 0.7908060
## NRN2.1 .
## NRN3.1 1.0182359
## NRN4.1 0.2028710
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -1.0914991
## NRE2.1 1.3058101
## NRE3.1 0.5351254
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.1965847
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.5063336
## NRA6.1 .
## NRC1.1 2.3473435
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.1606943
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.5076348# Test sample
bdi.overall.test.enr3 = bdi.overall.test.z[,c(1:31)]
pred.bdi.ov.enr3.test = predict.glmnet(bdi.overall.enr3, newx = data.matrix(bdi.overall.test.enr3))
postResample(pred.bdi.ov.enr3.test, bdi.overall.test$BDI2.2) # R2 = 10.55%, MAE = 8.61, RMSE = 11.17## RMSE Rsquared MAE
## 11.2201407 0.1068767 8.8061665#### Model 4: Demographics and FFM facets
iv.bdi.processed5 = iv.bdi.processed[,c(1:30, 32, 33)]
bdi.overall.train.z5 = bdi.overall.train.z[,c(1:30, 32, 33, 34)]
bdi.overall.enr5 = elastic.net(data = bdi.overall.train.z5, zscored.ivs = iv.bdi.processed5, outcome = bdi.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 10.4602 0.1248972 8.493389 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.5 1.3 10.4602 0.1248972 8.493389 0.9847665 0.1028125 0.7901621
## alpha lambda
## 109 0.5 1.3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.146058216
## NRN1.1 1.383107014
## NRN2.1 .
## NRN3.1 1.788551392
## NRN4.1 0.310131918
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.772004480
## NRE2.1 0.749877020
## NRE3.1 0.846344996
## NRE4.1 -0.005852021
## NRE5.1 0.074542586
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.401114092
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.880197747
## NRA6.1 .
## NRC1.1 1.947405138
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age -0.067744773# Test sample
bdi.overall.test.enr5 = bdi.overall.test.z[,c(1:30, 32, 33)]
pred.bdi.ov.enr5.test = predict.glmnet(bdi.overall.enr5, newx = data.matrix(bdi.overall.test.enr5))
postResample(pred.bdi.ov.enr5.test, bdi.overall.test$BDI2.2) # R2 = 2.46%, MAE = 9.30, RMSE = 11.80## RMSE Rsquared MAE
## 11.81343651 0.02754475 9.52350240#### Model 5: Every IV
iv.bdi.processed4 = iv.bdi.processed[,c(1:33)]
bdi.overall.train.z4 = bdi.overall.train.z[,c(1:34)]
bdi.overall.enr4 = elastic.net(data = bdi.overall.train.z4, zscored.ivs = iv.bdi.processed4, outcome = bdi.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 9.868224 0.2157514 8.059233 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.65 9.868224 0.2157514 8.059233 0.9637958 0.09642071 0.7495171
## alpha lambda
## 178 1 0.65
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.1460582
## NRN1.1 0.8172232
## NRN2.1 .
## NRN3.1 0.9751875
## NRN4.1 0.1554586
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.9898369
## NRE2.1 1.1776529
## NRE3.1 0.4973671
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.1204729
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.4460026
## NRA6.1 .
## NRC1.1 2.2645944
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.1072678
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.4778805
## sex .
## age .# Test sample
bdi.overall.test.enr4 = bdi.overall.test.z[,c(1:33)]
pred.bdi.ov.enr4.test = predict.glmnet(bdi.overall.enr4, newx = data.matrix(bdi.overall.test.enr4))
postResample(pred.bdi.ov.enr4.test, bdi.overall.test$BDI2.2) # R2 = 11.53%, MAE = 8.76, RMSE = 11.31## RMSE Rsquared MAE
## 11.1730126 0.1108534 8.78974505.3 HAM-D scores
## Splitting data into training and test sets
set.seed(2020)
hd.index = createDataPartition(neo.depr.hd$hamd2, p = .7, list = F)
hd.overall.train = neo.depr.hd[hd.index, ]
hd.overall.test = neo.depr.hd[-hd.index, ]
nrow(hd.overall.train)## [1] 229nrow(hd.overall.test)## [1] 965.3.1 Ordinary least squares
#### Pre-tx HAM-D only
hd.overall.lm1 = lm(hamd2 ~ hamd1, data = hd.overall.train)
summ(hd.overall.lm1)| Observations | 229 |
| Dependent variable | hamd2 |
| Type | OLS linear regression |
| F(1,227) | 26.55 |
| R² | 0.10 |
| Adj. R² | 0.10 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -0.59 | 1.86 | -0.32 | 0.75 |
| hamd1 | 0.48 | 0.09 | 5.15 | 0.00 |
| Standard errors: OLS |
# Test sample
pred.hd.ov.lm1.test = predict(hd.overall.lm1, hd.overall.test)
postResample(pred.hd.ov.lm1.test, hd.overall.test$hamd2) # R2 = 15.25%, MAE = 5.56, RMSE = 6.80## RMSE Rsquared MAE
## 6.8029665 0.1525282 5.5619167#### Model 1: Pre-tx HAM-D and demographics
hd.overall.lm2 = lm(hamd2 ~ hamd1 + age + sex, data = hd.overall.train)
summ(hd.overall.lm2)| Observations | 229 |
| Dependent variable | hamd2 |
| Type | OLS linear regression |
| F(3,225) | 9.41 |
| R² | 0.11 |
| Adj. R² | 0.10 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -1.86 | 2.35 | -0.79 | 0.43 |
| hamd1 | 0.46 | 0.09 | 4.94 | 0.00 |
| age | 0.02 | 0.04 | 0.54 | 0.59 |
| sex1 | 1.15 | 0.94 | 1.22 | 0.22 |
| Standard errors: OLS |
# Test sample
pred.hd.ov.lm2.test = predict(hd.overall.lm2, hd.overall.test)
postResample(pred.hd.ov.lm2.test, hd.overall.test$hamd2) # R2 = 16.04%, MAE = 5.56, RMSE = 6.77 ## RMSE Rsquared MAE
## 6.7659012 0.1603607 5.5593528#### Model 2: FFM facets only
hd.overall.train.facets = hd.overall.train[,c(1:30, 32)]
hd.overall.lm3 = lm(hamd2 ~ ., data = hd.overall.train.facets)
summ(hd.overall.lm3)| Observations | 229 |
| Dependent variable | hamd2 |
| Type | OLS linear regression |
| F(30,198) | 1.63 |
| R² | 0.20 |
| Adj. R² | 0.08 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -5.89 | 6.34 | -0.93 | 0.35 |
| NRN1.1 | 0.07 | 0.13 | 0.53 | 0.60 |
| NRN2.1 | 0.19 | 0.14 | 1.30 | 0.19 |
| NRN3.1 | 0.17 | 0.16 | 1.04 | 0.30 |
| NRN4.1 | -0.07 | 0.14 | -0.49 | 0.63 |
| NRN5.1 | -0.10 | 0.12 | -0.84 | 0.40 |
| NRN6.1 | 0.03 | 0.15 | 0.20 | 0.84 |
| NRE1.1 | 0.19 | 0.16 | 1.21 | 0.23 |
| NRE2.1 | -0.08 | 0.12 | -0.66 | 0.51 |
| NRE3.1 | 0.25 | 0.12 | 2.04 | 0.04 |
| NRE4.1 | -0.12 | 0.14 | -0.82 | 0.41 |
| NRE5.1 | 0.08 | 0.13 | 0.58 | 0.56 |
| NRE6.1 | -0.25 | 0.13 | -1.91 | 0.06 |
| NRO1.1 | 0.07 | 0.12 | 0.62 | 0.54 |
| NRO2.1 | -0.02 | 0.13 | -0.14 | 0.89 |
| NRO3.1 | -0.15 | 0.16 | -0.96 | 0.34 |
| NRO4.1 | -0.15 | 0.13 | -1.17 | 0.24 |
| NRO5.1 | 0.05 | 0.10 | 0.48 | 0.63 |
| NRO6.1 | -0.18 | 0.14 | -1.23 | 0.22 |
| NRA1.1 | -0.25 | 0.11 | -2.24 | 0.03 |
| NRA2.1 | -0.01 | 0.13 | -0.06 | 0.95 |
| NRA3.1 | 0.04 | 0.17 | 0.21 | 0.83 |
| NRA4.1 | 0.29 | 0.16 | 1.84 | 0.07 |
| NRA5.1 | 0.12 | 0.13 | 0.92 | 0.36 |
| NRA6.1 | 0.31 | 0.14 | 2.14 | 0.03 |
| NRC1.1 | 0.06 | 0.16 | 0.38 | 0.70 |
| NRC2.1 | 0.11 | 0.13 | 0.87 | 0.39 |
| NRC3.1 | -0.21 | 0.15 | -1.41 | 0.16 |
| NRC4.1 | 0.21 | 0.14 | 1.56 | 0.12 |
| NRC5.1 | 0.01 | 0.14 | 0.05 | 0.96 |
| NRC6.1 | 0.09 | 0.14 | 0.68 | 0.50 |
| Standard errors: OLS |
# Test sample
hd.overall.test.facets = hd.overall.test[,c(1:30, 32)]
pred.hd.ov.lm3.test = predict(hd.overall.lm3, hd.overall.test.facets)
postResample(pred.hd.ov.lm3.test, hd.overall.test.facets$hamd2) # R2 = 9.73%, MAE = 5.64, RMSE = 7.11 ## RMSE Rsquared MAE
## 7.33506650 0.06861291 5.83146160#### Model 3: Pre-tx HAM-D and FFM facets
hd.overall.train.facets2 = hd.overall.train[,c(1:32)]
hd.overall.lm4 = lm(hamd2 ~ ., data = hd.overall.train.facets2)
summ(hd.overall.lm4)| Observations | 229 |
| Dependent variable | hamd2 |
| Type | OLS linear regression |
| F(31,197) | 2.45 |
| R² | 0.28 |
| Adj. R² | 0.16 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -11.67 | 6.15 | -1.90 | 0.06 |
| NRN1.1 | -0.09 | 0.13 | -0.68 | 0.50 |
| NRN2.1 | 0.21 | 0.14 | 1.52 | 0.13 |
| NRN3.1 | 0.16 | 0.15 | 1.03 | 0.30 |
| NRN4.1 | -0.04 | 0.13 | -0.32 | 0.75 |
| NRN5.1 | -0.04 | 0.12 | -0.34 | 0.73 |
| NRN6.1 | -0.04 | 0.14 | -0.31 | 0.76 |
| NRE1.1 | 0.14 | 0.15 | 0.92 | 0.36 |
| NRE2.1 | -0.07 | 0.11 | -0.63 | 0.53 |
| NRE3.1 | 0.20 | 0.12 | 1.66 | 0.10 |
| NRE4.1 | -0.07 | 0.14 | -0.49 | 0.62 |
| NRE5.1 | 0.09 | 0.12 | 0.72 | 0.47 |
| NRE6.1 | -0.30 | 0.12 | -2.48 | 0.01 |
| NRO1.1 | 0.09 | 0.11 | 0.80 | 0.43 |
| NRO2.1 | -0.00 | 0.12 | -0.00 | 1.00 |
| NRO3.1 | -0.16 | 0.15 | -1.06 | 0.29 |
| NRO4.1 | -0.09 | 0.13 | -0.73 | 0.47 |
| NRO5.1 | 0.05 | 0.09 | 0.57 | 0.57 |
| NRO6.1 | -0.18 | 0.14 | -1.33 | 0.19 |
| NRA1.1 | -0.26 | 0.11 | -2.46 | 0.01 |
| NRA2.1 | -0.08 | 0.12 | -0.63 | 0.53 |
| NRA3.1 | 0.09 | 0.16 | 0.56 | 0.57 |
| NRA4.1 | 0.26 | 0.15 | 1.72 | 0.09 |
| NRA5.1 | 0.08 | 0.13 | 0.62 | 0.53 |
| NRA6.1 | 0.28 | 0.14 | 2.05 | 0.04 |
| NRC1.1 | 0.09 | 0.16 | 0.54 | 0.59 |
| NRC2.1 | 0.10 | 0.12 | 0.84 | 0.40 |
| NRC3.1 | -0.18 | 0.14 | -1.32 | 0.19 |
| NRC4.1 | 0.19 | 0.13 | 1.50 | 0.13 |
| NRC5.1 | -0.03 | 0.13 | -0.22 | 0.82 |
| NRC6.1 | 0.17 | 0.13 | 1.29 | 0.20 |
| hamd1 | 0.49 | 0.11 | 4.67 | 0.00 |
| Standard errors: OLS |
# Test sample
hd.overall.test.facets2 = hd.overall.test[,c(1:32)]
pred.hd.ov.lm4.test = predict(hd.overall.lm4, hd.overall.test.facets2)
postResample(pred.hd.ov.lm4.test, hd.overall.test.facets2$hamd2) # R2 = 16.72%, MAE = 5.49, RMSE = 6.89 ## RMSE Rsquared MAE
## 7.0906274 0.1391418 5.6268563#### Model 4: Demographics and FFM facets
hd.overall.train.facets3 = hd.overall.train[,c(1:30, 35, 36, 32)]
hd.overall.lm6 = lm(hamd2 ~ ., data = hd.overall.train.facets3)
summ(hd.overall.lm6)| Observations | 229 |
| Dependent variable | hamd2 |
| Type | OLS linear regression |
| F(32,196) | 1.60 |
| R² | 0.21 |
| Adj. R² | 0.08 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -7.57 | 6.53 | -1.16 | 0.25 |
| NRN1.1 | 0.07 | 0.13 | 0.53 | 0.60 |
| NRN2.1 | 0.16 | 0.14 | 1.12 | 0.26 |
| NRN3.1 | 0.17 | 0.16 | 1.04 | 0.30 |
| NRN4.1 | -0.05 | 0.14 | -0.36 | 0.72 |
| NRN5.1 | -0.13 | 0.12 | -1.03 | 0.31 |
| NRN6.1 | 0.04 | 0.15 | 0.24 | 0.81 |
| NRE1.1 | 0.16 | 0.16 | 0.98 | 0.33 |
| NRE2.1 | -0.05 | 0.12 | -0.38 | 0.70 |
| NRE3.1 | 0.25 | 0.12 | 1.96 | 0.05 |
| NRE4.1 | -0.11 | 0.14 | -0.75 | 0.46 |
| NRE5.1 | 0.12 | 0.13 | 0.87 | 0.38 |
| NRE6.1 | -0.23 | 0.13 | -1.81 | 0.07 |
| NRO1.1 | 0.07 | 0.12 | 0.63 | 0.53 |
| NRO2.1 | -0.01 | 0.13 | -0.09 | 0.93 |
| NRO3.1 | -0.14 | 0.16 | -0.91 | 0.36 |
| NRO4.1 | -0.19 | 0.14 | -1.44 | 0.15 |
| NRO5.1 | 0.07 | 0.10 | 0.67 | 0.50 |
| NRO6.1 | -0.15 | 0.14 | -1.05 | 0.30 |
| NRA1.1 | -0.26 | 0.11 | -2.30 | 0.02 |
| NRA2.1 | -0.02 | 0.13 | -0.18 | 0.86 |
| NRA3.1 | 0.04 | 0.17 | 0.23 | 0.82 |
| NRA4.1 | 0.27 | 0.16 | 1.67 | 0.10 |
| NRA5.1 | 0.12 | 0.13 | 0.91 | 0.36 |
| NRA6.1 | 0.29 | 0.14 | 2.02 | 0.04 |
| NRC1.1 | 0.05 | 0.16 | 0.30 | 0.76 |
| NRC2.1 | 0.09 | 0.13 | 0.70 | 0.49 |
| NRC3.1 | -0.24 | 0.15 | -1.59 | 0.11 |
| NRC4.1 | 0.20 | 0.14 | 1.47 | 0.14 |
| NRC5.1 | 0.02 | 0.14 | 0.17 | 0.86 |
| NRC6.1 | 0.10 | 0.14 | 0.74 | 0.46 |
| sex1 | 0.98 | 1.13 | 0.86 | 0.39 |
| age | 0.07 | 0.05 | 1.27 | 0.21 |
| Standard errors: OLS |
# Test sample
hd.overall.test.facets3 = hd.overall.test[,c(1:30, 35, 36, 32)]
pred.hd.ov.lm6.test = predict(hd.overall.lm6, hd.overall.test.facets3)
postResample(pred.hd.ov.lm6.test, hd.overall.test$hamd2) # R2 = 8.47%, MAE = 5.70, RMSE = 7.17## RMSE Rsquared MAE
## 7.34097558 0.06313444 5.85499044#### Model 5: Every IV
hd.overall.train2 = hd.overall.train[,c(1:32, 35, 36)]
hd.overall.lm5 = lm(hamd2 ~ ., data = hd.overall.train2)
summ(hd.overall.lm5)| Observations | 229 |
| Dependent variable | hamd2 |
| Type | OLS linear regression |
| F(33,195) | 2.34 |
| R² | 0.28 |
| Adj. R² | 0.16 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -12.49 | 6.32 | -1.98 | 0.05 |
| NRN1.1 | -0.09 | 0.13 | -0.67 | 0.50 |
| NRN2.1 | 0.19 | 0.14 | 1.41 | 0.16 |
| NRN3.1 | 0.16 | 0.15 | 1.04 | 0.30 |
| NRN4.1 | -0.03 | 0.13 | -0.20 | 0.84 |
| NRN5.1 | -0.06 | 0.12 | -0.55 | 0.59 |
| NRN6.1 | -0.04 | 0.14 | -0.30 | 0.76 |
| NRE1.1 | 0.11 | 0.16 | 0.67 | 0.50 |
| NRE2.1 | -0.05 | 0.11 | -0.42 | 0.67 |
| NRE3.1 | 0.19 | 0.12 | 1.62 | 0.11 |
| NRE4.1 | -0.06 | 0.14 | -0.45 | 0.65 |
| NRE5.1 | 0.12 | 0.13 | 0.94 | 0.35 |
| NRE6.1 | -0.29 | 0.12 | -2.33 | 0.02 |
| NRO1.1 | 0.09 | 0.11 | 0.79 | 0.43 |
| NRO2.1 | 0.00 | 0.12 | 0.03 | 0.98 |
| NRO3.1 | -0.16 | 0.15 | -1.06 | 0.29 |
| NRO4.1 | -0.13 | 0.13 | -0.98 | 0.33 |
| NRO5.1 | 0.07 | 0.10 | 0.73 | 0.46 |
| NRO6.1 | -0.17 | 0.14 | -1.21 | 0.23 |
| NRA1.1 | -0.26 | 0.11 | -2.42 | 0.02 |
| NRA2.1 | -0.09 | 0.12 | -0.70 | 0.49 |
| NRA3.1 | 0.10 | 0.16 | 0.60 | 0.55 |
| NRA4.1 | 0.25 | 0.15 | 1.61 | 0.11 |
| NRA5.1 | 0.07 | 0.13 | 0.59 | 0.55 |
| NRA6.1 | 0.27 | 0.14 | 1.94 | 0.05 |
| NRC1.1 | 0.07 | 0.16 | 0.45 | 0.65 |
| NRC2.1 | 0.09 | 0.13 | 0.68 | 0.50 |
| NRC3.1 | -0.20 | 0.14 | -1.41 | 0.16 |
| NRC4.1 | 0.18 | 0.13 | 1.43 | 0.15 |
| NRC5.1 | -0.02 | 0.13 | -0.15 | 0.88 |
| NRC6.1 | 0.18 | 0.13 | 1.32 | 0.19 |
| hamd1 | 0.49 | 0.11 | 4.57 | 0.00 |
| sex1 | 1.06 | 1.08 | 0.98 | 0.33 |
| age | 0.04 | 0.05 | 0.83 | 0.41 |
| Standard errors: OLS |
pred.hd.ov.lm5.train = predict(hd.overall.lm5, hd.overall.train2)
postResample(pred.hd.ov.lm5.train, hd.overall.train2$hamd2) ## RMSE Rsquared MAE
## 6.2212463 0.2836152 5.1818172# Test sample
hd.overall.test2 = hd.overall.test[,c(1:32, 35, 36)]
pred.hd.ov.lm5.test = predict(hd.overall.lm5, hd.overall.test2)
postResample(pred.hd.ov.lm5.test, hd.overall.test$hamd2) # R2 = 16.74%, MAE = 5.53, RMSE = 6.85 ## RMSE Rsquared MAE
## 7.0136459 0.1433653 5.62736195.3.2 Elastic net regression
#### Standardizing IVs
# Training set
iv.hd.process = subset(hd.overall.train, select = iv.hd.names)
iv.hd.processed = standardizing(data = iv.hd.process, var.info = hd.info)
hd.overall.train.z = iv.hd.processed
hd.overall.train.z$y = hd.overall.train$hamd2
hd.overall.train.outcome = select(hd.overall.train, hamd2)
# Test set
iv.hd.process.test = subset(hd.overall.test, select = iv.hd.names)
iv.hd.processed.test = standardizing(data = iv.hd.process.test, var.info = hd.info)
hd.overall.test.z = iv.hd.processed.test
hd.overall.test.z$y = hd.overall.test$hamd2
hd.overall.test.outcome = select(hd.overall.test, hamd2)
#### Model 1: Pre-tx HAM-D and demographics only
iv.hd.processed1 = iv.hd.processed[,c(31:33)]
hd.overall.train.z1 = hd.overall.train.z[,c(31:34)]
hd.overall.enr1 = elastic.net(data = hd.overall.train.z1, zscored.ivs = iv.hd.processed1, outcome = hd.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 7.014748 0.1113871 5.916731 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0 1.1 7.014748 0.1113871 5.916731 0.4730736 0.07775462 0.3207513
## alpha lambda
## 23 0 1.1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.074428
## hamd1 2.016651
## sex 1.032756
## age 0.254257# Test sample
hd.overall.test.enr1 = hd.overall.test.z[,c(31:33)]
pred.hd.ov.enr1.test = predict.glmnet(hd.overall.enr1, newx = data.matrix(hd.overall.test.enr1))
postResample(pred.hd.ov.enr1.test, hd.overall.test$hamd2) # R2 = 16.02%, MAE = 5.56, RMSE = 6.80## RMSE Rsquared MAE
## 6.7987612 0.1602061 5.5594473#### Model 2: FFM facets only
iv.hd.processed2 = iv.hd.processed[,c(1:30)]
hd.overall.train.z2 = hd.overall.train.z[,c(1:30, 34)]
hd.overall.enr2 = elastic.net(data = hd.overall.train.z2, zscored.ivs = iv.hd.processed2, outcome = hd.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 7.249307 0.04809911 6.24029 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.25 1.3 7.249307 0.04809911 6.24029 0.5221866 0.0503298 0.3772145
## alpha lambda
## 68 0.25 1.3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.72052402
## NRN1.1 0.36497646
## NRN2.1 0.20177301
## NRN3.1 0.27699985
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.10084937
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.37216768
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.71921494
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.30952934
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.61909222
## NRA2.1 .
## NRA3.1 0.21717395
## NRA4.1 0.26863090
## NRA5.1 0.11329916
## NRA6.1 0.63461636
## NRC1.1 .
## NRC2.1 0.08821937
## NRC3.1 .
## NRC4.1 0.49731545
## NRC5.1 .
## NRC6.1 0.40006603# Test sample
hd.overall.test.enr2 = hd.overall.test.z[,c(1:30)]
pred.hd.ov.enr2.test = predict.glmnet(hd.overall.enr2, newx = data.matrix(hd.overall.test.enr2))
postResample(pred.hd.ov.enr2.test, hd.overall.test$hamd2) # R2 = 12.16%, MAE = 5.56, RMSE = 6.94## RMSE Rsquared MAE
## 7.04167980 0.08959697 5.66146305#### Model 3: Pre-tx HAM-D and FFM facets
iv.hd.processed3 = iv.hd.processed[,c(1:31)]
hd.overall.train.z3 = hd.overall.train.z[,c(1:31, 34)]
hd.overall.enr3 = elastic.net(data = hd.overall.train.z3, zscored.ivs = iv.hd.processed3, outcome = hd.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 6.898048 0.1401469 5.805441 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.4 6.898048 0.1401469 5.805441 0.4663919 0.08621053 0.3624907
## alpha lambda
## 173 1 0.4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.72052402
## NRN1.1 .
## NRN2.1 0.03100498
## NRN3.1 0.07139730
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.12898376
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.94254019
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.01190978
## NRO5.1 0.09148474
## NRO6.1 .
## NRA1.1 -0.85271195
## NRA2.1 .
## NRA3.1 0.16998136
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.58155174
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.51988696
## NRC5.1 .
## NRC6.1 0.49504542
## hamd1 2.02746231# Test sample
hd.overall.test.enr3 = hd.overall.test.z[,c(1:31)]
pred.hd.ov.enr3.test = predict.glmnet(hd.overall.enr3, newx = data.matrix(hd.overall.test.enr3))
postResample(pred.hd.ov.enr3.test, hd.overall.test$hamd2) # R2 = 17.49%, MAE = 5.37, RMSE = 6.70## RMSE Rsquared MAE
## 6.7241304 0.1690668 5.3974028#### Model 4: Demographics and FFM facets
iv.hd.processed5 = iv.hd.processed[,c(1:30, 32, 33)]
hd.overall.train.z5 = hd.overall.train.z[,c(1:30, 32, 33, 34)]
hd.overall.enr5 = elastic.net(data = hd.overall.train.z5, zscored.ivs = iv.hd.processed5, outcome = hd.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 7.27816 0.0386162 6.279228 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.25 1.4 7.27816 0.0386162 6.279228 0.5330102 0.03802362 0.3817121
## alpha lambda
## 70 0.25 1.4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.42693476
## NRN1.1 0.37936813
## NRN2.1 0.16135311
## NRN3.1 0.27300405
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.09385890
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.30813503
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.66122916
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.28633564
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.59860297
## NRA2.1 .
## NRA3.1 0.19930742
## NRA4.1 0.19412765
## NRA5.1 0.08646308
## NRA6.1 0.59425514
## NRC1.1 .
## NRC2.1 0.06925236
## NRC3.1 .
## NRC4.1 0.45875071
## NRC5.1 .
## NRC6.1 0.38352772
## sex 0.18419710
## age 0.15099654# Test sample
hd.overall.test.enr5 = hd.overall.test.z[,c(1:30, 32, 33)]
pred.hd.ov.enr5.test = predict.glmnet(hd.overall.enr5, newx = data.matrix(hd.overall.test.enr5))
postResample(pred.hd.ov.enr5.test, hd.overall.test$hamd2) # R2 = 11.70%, MAE = 5.55, RMSE = 6.95## RMSE Rsquared MAE
## 7.0432765 0.0912558 5.6690508#### Model 5: Every IV
iv.hd.processed4 = iv.hd.processed[,c(1:33)]
hd.overall.train.z4 = hd.overall.train.z[,c(1:34)]
hd.overall.enr4 = elastic.net(data = hd.overall.train.z4, zscored.ivs = iv.hd.processed4, outcome = hd.overall.train.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 6.918722 0.1342491 5.833445 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.4 6.918722 0.1342491 5.833445 0.4779073 0.08408782 0.3765079
## alpha lambda
## 173 1 0.4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.66775649
## NRN1.1 .
## NRN2.1 0.02880015
## NRN3.1 0.07073381
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.12848093
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.94234213
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.01672459
## NRO5.1 0.09340154
## NRO6.1 .
## NRA1.1 -0.85158909
## NRA2.1 .
## NRA3.1 0.16841718
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.58083554
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.51847548
## NRC5.1 .
## NRC6.1 0.49419772
## hamd1 2.02707977
## sex 0.03310620
## age .# Test sample
hd.overall.test.enr4 = hd.overall.test.z[,c(1:33)]
pred.hd.ov.enr4.test = predict.glmnet(hd.overall.enr4, newx = data.matrix(hd.overall.test.enr4))
postResample(pred.hd.ov.enr4.test, hd.overall.test$hamd2) # R2 = 17.68%, MAE = 5.37, RMSE = 6.70## RMSE Rsquared MAE
## 6.7221535 0.1696352 5.39690376 Personalized Advantage Index (differential treatment response)
6.1 Functions
## Cross-validation settings
ctrl.pai = trainControl(method = "cv", # K-fold CV due to computational demands
number = 5, # 5-fold CV
savePredictions = T,
verbose = F)
## Elastic net regression settings - modifying the output and the CV procedure
elastic.net.pai = function(data, zscored.ivs, outcome){
eGrid = expand.grid(.alpha = seq(0, 1, by = .25), .lambda = seq(0, 2, by = .05)) # Parameter space for possible hyperparameters
set.seed(2020)
model = caret::train(y ~ .,
data = data,
method = "glmnet",
metric = "RMSE", # Pick the model with the lowest RMSE
tuneGrid = eGrid,
trControl = ctrl.pai)
final.model = glmnet::glmnet(data.matrix(zscored.ivs), as.matrix(outcome), alpha = model$bestTune["alpha"],
lambda = model$bestTune["lambda"]) # Fitting the ENR model with best hyperparameters
print(coef(final.model))
return(final.model)
}
## Ordinary least squares regression function
regression.pai = function(data){
set.seed(2020)
model = caret::train(y ~ .,
data = data,
method = "lm",
trControl = ctrl.pai)
return(model)
}6.2 BDI-II
## Creating datasets, one for each tx group
iv.bdi.process.cbt = subset(neo.depr.cbt.bdi, select = iv.bdi.names)
iv.bdi.processed.cbt = standardizing(data = iv.bdi.process.cbt, var.info = bdi.info)
bdi.cbt.z = iv.bdi.processed.cbt
bdi.cbt.z$y = neo.depr.cbt.bdi$BDI2.2
bdi.cbt.outcome = select(neo.depr.cbt.bdi, BDI2.2)
iv.bdi.process.med = subset(neo.depr.med.bdi, select = iv.bdi.names)
iv.bdi.processed.med = standardizing(data = iv.bdi.process.med, var.info = bdi.info)
bdi.med.z = iv.bdi.processed.med
bdi.med.z$y = neo.depr.med.bdi$BDI2.2
bdi.med.outcome = select(neo.depr.med.bdi, BDI2.2)
## Creating datasets based on each submodel
# Model 1: Pre-tx BDI-II and demographics
iv.bdi.processed.cbt1 = iv.bdi.processed.cbt[,c(31:33)]
bdi.cbt.z1 = iv.bdi.processed.cbt1
bdi.cbt.z1$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.reg.cbt1 = iv.bdi.process.cbt[,c(31:33)]
bdi.cbt.reg1 = iv.bdi.reg.cbt1
bdi.cbt.reg1$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.processed.med1 = iv.bdi.processed.med[,c(31:33)]
bdi.med.z1 = iv.bdi.processed.med1
bdi.med.z1$y = neo.depr.med.bdi$BDI2.2
iv.bdi.reg.med1 = iv.bdi.process.med[,c(31:33)]
bdi.med.reg1 = iv.bdi.reg.med1
bdi.med.reg1$y = neo.depr.med.bdi$BDI2.2
# Model 2: FFM facets only
iv.bdi.processed.cbt2 = iv.bdi.processed.cbt[,c(1:30)]
bdi.cbt.z2 = iv.bdi.processed.cbt2
bdi.cbt.z2$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.reg.cbt2 = iv.bdi.process.cbt[,c(1:30)]
bdi.cbt.reg2 = iv.bdi.reg.cbt2
bdi.cbt.reg2$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.processed.med2 = iv.bdi.processed.med[,c(1:30)]
bdi.med.z2 = iv.bdi.processed.med2
bdi.med.z2$y = neo.depr.med.bdi$BDI2.2
iv.bdi.reg.med2 = iv.bdi.process.med[,c(1:30)]
bdi.med.reg2 = iv.bdi.reg.med2
bdi.med.reg2$y = neo.depr.med.bdi$BDI2.2
# Model 3: Demographics and FFM facets
iv.bdi.processed.cbt3 = iv.bdi.processed.cbt[,c(1:30, 32, 33)]
bdi.cbt.z3 = iv.bdi.processed.cbt3
bdi.cbt.z3$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.reg.cbt3 = iv.bdi.process.cbt[,c(1:30, 32, 33)]
bdi.cbt.reg3 = iv.bdi.reg.cbt3
bdi.cbt.reg3$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.processed.med3 = iv.bdi.processed.med[,c(1:30, 32, 33)]
bdi.med.z3 = iv.bdi.processed.med3
bdi.med.z3$y = neo.depr.med.bdi$BDI2.2
iv.bdi.reg.med3 = iv.bdi.process.med[,c(1:30, 32, 33)]
bdi.med.reg3 = iv.bdi.reg.med3
bdi.med.reg3$y = neo.depr.med.bdi$BDI2.2
# Model 4: Pre-tx BDI-II and FFM facets
iv.bdi.processed.cbt4 = iv.bdi.processed.cbt[,c(1:31)]
bdi.cbt.z4 = iv.bdi.processed.cbt4
bdi.cbt.z4$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.reg.cbt4 = iv.bdi.process.cbt[,c(1:31)]
bdi.cbt.reg4 = iv.bdi.reg.cbt4
bdi.cbt.reg4$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.processed.med4 = iv.bdi.processed.med[,c(1:31)]
bdi.med.z4 = iv.bdi.processed.med4
bdi.med.z4$y = neo.depr.med.bdi$BDI2.2
iv.bdi.reg.med4 = iv.bdi.process.med[,c(1:31)]
bdi.med.reg4 = iv.bdi.reg.med4
bdi.med.reg4$y = neo.depr.med.bdi$BDI2.2
# Model 5: Every IV
iv.bdi.processed.cbt5 = iv.bdi.processed.cbt
bdi.cbt.z5 = iv.bdi.processed.cbt5
bdi.cbt.z5$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.reg.cbt5 = iv.bdi.process.cbt
bdi.cbt.reg5 = iv.bdi.reg.cbt5
bdi.cbt.reg5$y = neo.depr.cbt.bdi$BDI2.2
iv.bdi.processed.med5 = iv.bdi.processed.med
bdi.med.z5 = iv.bdi.processed.med5
bdi.med.z5$y = neo.depr.med.bdi$BDI2.2
iv.bdi.reg.med5 = iv.bdi.process.med
bdi.med.reg5 = iv.bdi.reg.med5
bdi.med.reg5$y = neo.depr.med.bdi$BDI2.26.2.1 Elastic net regression models for counterfactual estimates
bdi.cbt.model1 = elastic.net(data = bdi.cbt.z1, zscored.ivs = iv.bdi.processed.cbt1, outcome = bdi.cbt.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 9.63548 0.1510654 7.917387 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0 2 9.63548 0.1510654 7.917387 1.483441 0.1706722 1.13075
## alpha lambda
## 41 0 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.917596
## BDI2.1 2.513778
## sex 2.103184
## age 1.574235bdi.med.model1 = elastic.net(data = bdi.med.z1, zscored.ivs = iv.bdi.processed.med1, outcome = bdi.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 10.88228 0.2010097 8.839504 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.85 10.88228 0.2010097 8.839504 1.075619 0.1519658 0.9332769
## alpha lambda
## 182 1 0.85
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.910314
## BDI2.1 4.296785
## sex .
## age .bdi.cbt.model2 = elastic.net(data = bdi.cbt.z2, zscored.ivs = iv.bdi.processed.cbt2, outcome = bdi.cbt.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 8.930185 0.2858927 7.082708 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.75 1.2 8.930185 0.2858927 7.082708 1.485025 0.1654172 1.214335
## alpha lambda
## 148 0.75 1.2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.2874714
## NRN1.1 2.5298381
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1250175
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.0281218
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.9586714
## NRA3.1 .
## NRA4.1 0.9287456
## NRA5.1 1.6063059
## NRA6.1 -1.3408098
## NRC1.1 0.4718289
## NRC2.1 0.4084037
## NRC3.1 .
## NRC4.1 0.6369591
## NRC5.1 .
## NRC6.1 .bdi.med.model2 = elastic.net(data = bdi.med.z2, zscored.ivs = iv.bdi.processed.med2, outcome = bdi.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 11.81049 0.05531037 9.757631 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 2 11.81049 0.05531037 9.757631 1.15067 0.05523338 0.8120972
## alpha lambda
## 205 1 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.910314
## NRN1.1 1.091736
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .bdi.cbt.model3 = elastic.net(data = bdi.cbt.z3, zscored.ivs = iv.bdi.processed.cbt3, outcome = bdi.cbt.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 8.973541 0.2796406 7.122527 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.75 1.25 8.973541 0.2796406 7.122527 1.482034 0.1701217 1.217031
## alpha lambda
## 149 0.75 1.25
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.2874714
## NRN1.1 2.4891315
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1106044
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.9923552
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.9616604
## NRA3.1 .
## NRA4.1 0.9140380
## NRA5.1 1.5605924
## NRA6.1 -1.2887450
## NRC1.1 0.4376655
## NRC2.1 0.4030431
## NRC3.1 .
## NRC4.1 0.6311753
## NRC5.1 .
## NRC6.1 .
## sex .
## age .bdi.med.model3 = elastic.net(data = bdi.med.z3, zscored.ivs = iv.bdi.processed.med3, outcome = bdi.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 11.81764 0.05464182 9.760989 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 2 11.81764 0.05464182 9.760989 1.158643 0.05563929 0.8133817
## alpha lambda
## 205 1 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.910314
## NRN1.1 1.091736
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .bdi.cbt.model4 = elastic.net(data = bdi.cbt.z4, zscored.ivs = iv.bdi.processed.cbt4, outcome = bdi.cbt.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 8.784112 0.3084097 7.047238 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.5 1.65 8.784112 0.3084097 7.047238 1.496222 0.1790422 1.152928
## alpha lambda
## 116 0.5 1.65
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.28747137
## NRN1.1 1.81853243
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.08647157
## NRE2.1 .
## NRE3.1 0.26655128
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.92209533
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.70258620
## NRA3.1 .
## NRA4.1 1.37730506
## NRA5.1 1.47407589
## NRA6.1 -1.18991973
## NRC1.1 0.45374805
## NRC2.1 0.40106128
## NRC3.1 .
## NRC4.1 0.90354789
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.56424772bdi.med.model4 = elastic.net(data = bdi.med.z4, zscored.ivs = iv.bdi.processed.med4, outcome = bdi.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 11.00435 0.1926262 8.886052 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 1.55 11.00435 0.1926262 8.886052 1.102702 0.1633272 0.9391377
## alpha lambda
## 196 1 1.55
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.91031386
## NRN1.1 0.46405285
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.09231185
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.66776424
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.62645110bdi.cbt.model5 = elastic.net(data = bdi.cbt.z5, zscored.ivs = iv.bdi.processed.cbt5, outcome = bdi.cbt.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 8.805587 0.3054007 7.050514 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.5 1.65 8.805587 0.3054007 7.050514 1.499788 0.181417 1.163489
## alpha lambda
## 116 0.5 1.65
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.28747137
## NRN1.1 1.81842689
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.08658778
## NRE2.1 .
## NRE3.1 0.26406091
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.92457781
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.69779431
## NRA3.1 .
## NRA4.1 1.37674441
## NRA5.1 1.47421171
## NRA6.1 -1.19059402
## NRC1.1 0.45384740
## NRC2.1 0.40037362
## NRC3.1 .
## NRC4.1 0.90387604
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.56387504
## sex .
## age 0.01343362bdi.med.model5 = elastic.net(data = bdi.med.z5, zscored.ivs = iv.bdi.processed.med5, outcome = bdi.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 11.00543 0.1943506 8.894214 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 1.6 11.00543 0.1943506 8.894214 1.098824 0.1640768 0.9331035
## alpha lambda
## 197 1 1.6
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.91031386
## NRN1.1 0.42346048
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.05005911
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.61892751
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.57102770
## sex .
## age .6.2.2 Ordinary least squares models for counterfactual estimates
bdi.cbt.lm1 = lm(y ~ ., data = bdi.cbt.reg1)
bdi.med.lm1 = lm(y ~ ., data = bdi.med.reg1)
bdi.cbt.lm2 = lm(y ~ ., data = bdi.cbt.reg2)
bdi.med.lm2 = lm(y ~ ., data = bdi.med.reg2)
bdi.cbt.lm3 = lm(y ~ ., data = bdi.cbt.reg3)
bdi.med.lm3 = lm(y ~ ., data = bdi.med.reg3)
bdi.cbt.lm4 = lm(y ~ ., data = bdi.cbt.reg4)
bdi.med.lm4 = lm(y ~ ., data = bdi.med.reg4)
bdi.cbt.lm5 = lm(y ~ ., data = bdi.cbt.reg5)
bdi.med.lm5 = lm(y ~ ., data = bdi.med.reg5)
bdi.cbt.lm6 = lm(y ~ BDI2.1, data = bdi.cbt.reg5)
bdi.med.lm6 = lm(y ~ BDI2.1, data = bdi.med.reg5)6.2.3 Preparing data and matrices for PAIs
cv.num = 10 # Number of times to run full CV
bdi.cbt.z$txgrp = neo.depr.cbt.bdi$txgrp
bdi.med.z$txgrp = neo.depr.med.bdi$txgrp
bdi.pai = rbind(bdi.cbt.z, bdi.med.z)
cv.tx = as.factor(bdi.pai$txgrp)
cv.tx2 = as.factor(neo.depr.bdi$txgrp)
##### Generating matrices to save results ####
mult.pred.m1.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.m2.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.m3.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.m4.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.m5.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.lm.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.lm1.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.lm2.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.lm3.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.lm4.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)
mult.pred.lm5.bdi = matrix(NA, nrow = nrow(bdi.pai), ncol = 10)6.2.4 PAI analyses (estimates and follow-ups)
######### PAI results ###########
#### Model 1 (Pre-tx BDI-II and demographics)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(bdi.pai) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(bdi.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(bdi.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = bdi.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(31:34)] # Taking out txgrp variable
X_train_cbt_enfunc = X_train_cbt_data[,c(1:3)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = bdi.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(31:33)] # Taking out txgrp variable and y
X_train_med_data = bdi.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(31:34)]
X_train_med_enfunc = X_train_med_data[,c(1:3)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = bdi.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(31:33)]
bdi.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
bdi.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(bdi.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(bdi.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(bdi.cbt.model1, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(bdi.med.model1, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m1.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.807275
## BDI2.1 2.649582
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.091286
## BDI2.1 4.212254
## sex 1.079120
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.863412
## BDI2.1 2.751189
## sex 1.722354
## age 1.770876
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.47891
## BDI2.1 4.63781
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.608667
## BDI2.1 2.460256
## sex 2.181798
## age 1.201806
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.282140
## BDI2.1 3.495185
## sex 0.480357
## age -0.568100
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.694394
## BDI2.1 2.361971
## sex 3.023021
## age 2.263804
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.44458
## BDI2.1 4.86162
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.530649
## BDI2.1 1.738127
## sex 3.178161
## age 1.964947
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.809026
## BDI2.1 4.199091
## sex .
## age .
## running cv # 2
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.658652
## BDI2.1 1.718872
## sex 3.512769
## age 1.769672
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.43521508
## BDI2.1 4.47104472
## sex .
## age -0.09106828
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.913536
## BDI2.1 3.097670
## sex 1.440080
## age 1.117163
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.648422
## BDI2.1 3.749645
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.329613
## BDI2.1 1.836722
## sex 2.322922
## age 1.172558
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.797412
## BDI2.1 4.568326
## sex .
## age .
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.871001
## BDI2.1 2.796766
## sex 1.038797
## age 1.824298
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.584352
## BDI2.1 3.174988
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.744078
## BDI2.1 2.709962
## sex 2.293979
## age 2.017011
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.979556
## BDI2.1 3.277847
## sex .
## age .
## running cv # 3
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.5247714
## BDI2.1 2.7653486
## sex 0.4723208
## age 1.0275489
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7334524
## BDI2.1 5.0959130
## sex .
## age -0.4674935
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.4504663
## BDI2.1 2.6787029
## sex 2.6266648
## age 0.4053327
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.113474
## BDI2.1 4.337396
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.234487
## BDI2.1 2.332106
## sex 1.617618
## age 1.635972
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.9505188
## BDI2.1 3.2331108
## sex 1.8402967
## age -0.6982818
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.792491
## BDI2.1 2.291048
## sex 2.112212
## age 2.372100
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 18.1445974
## BDI2.1 5.1096816
## sex .
## age -0.1151648
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.480091
## BDI2.1 2.375342
## sex 3.198215
## age 2.042433
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.609975
## BDI2.1 4.047456
## sex .
## age .
## running cv # 4
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.023314
## BDI2.1 2.019895
## sex 2.170363
## age 1.750961
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.497437
## BDI2.1 5.168999
## sex .
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.845494
## BDI2.1 3.269374
## sex 1.726909
## age 1.772669
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.165427
## BDI2.1 3.950657
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.507673
## BDI2.1 2.240518
## sex 2.190548
## age 1.817850
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.1519539
## BDI2.1 3.8058117
## sex 0.6421842
## age -1.1108207
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.412590
## BDI2.1 2.164115
## sex 2.092078
## age 2.006287
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.363002
## BDI2.1 3.870168
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.2627065
## BDI2.1 2.9871128
## sex 2.1230022
## age 0.7746903
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.353424
## BDI2.1 3.782123
## sex .
## age .
## running cv # 5
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.054743
## BDI2.1 1.762216
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.857761
## BDI2.1 2.053572
## sex .
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.061664
## BDI2.1 1.956345
## sex 2.464469
## age 1.421351
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.516409
## BDI2.1 3.982012
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.7065135
## BDI2.1 2.0091198
## sex .
## age 0.7248287
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.330809
## BDI2.1 4.783328
## sex .
## age .
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.436949
## BDI2.1 2.877688
## sex 3.578291
## age 1.628428
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.964391
## BDI2.1 3.577772
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.208610
## BDI2.1 2.288632
## sex 2.571268
## age 2.340993
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.799037
## BDI2.1 4.115122
## sex .
## age .
## running cv # 6
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.8165751
## BDI2.1 2.3812578
## sex 0.7014129
## age 1.6325440
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.05482403
## BDI2.1 4.58668231
## sex .
## age -0.06934016
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.195254
## BDI2.1 2.229395
## sex 2.678354
## age 1.587223
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 18.797642
## BDI2.1 3.977209
## sex -1.029556
## age -1.206579
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.066277
## BDI2.1 2.612777
## sex 1.926398
## age 1.683283
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.473661
## BDI2.1 3.421074
## sex .
## age .
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.519424
## BDI2.1 2.748641
## sex 1.960092
## age 1.191504
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.92498
## BDI2.1 2.69569
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.766346
## BDI2.1 2.829788
## sex 3.366731
## age 1.961712
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.397521
## BDI2.1 7.151392
## sex 2.298748
## age 1.082797
## running cv # 7
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.2209874
## BDI2.1 3.6971352
## sex 1.2670303
## age 0.9502216
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.980611
## BDI2.1 4.557664
## sex .
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.335069
## BDI2.1 3.120461
## sex 2.312261
## age 2.097721
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.678278
## BDI2.1 4.564996
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.357318
## BDI2.1 1.652603
## sex 1.615300
## age 1.946265
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.692008
## BDI2.1 3.273472
## sex .
## age -1.038947
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.900776
## BDI2.1 1.753966
## sex 3.587470
## age 2.015608
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.235677
## BDI2.1 4.725905
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.561207
## BDI2.1 2.897264
## sex 1.950754
## age 1.235127
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.905179
## BDI2.1 3.432256
## sex .
## age .
## running cv # 8
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.539445
## BDI2.1 2.338311
## sex .
## age 1.687276
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.0980501
## BDI2.1 4.4215640
## sex 0.2933944
## age -0.7313610
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.666794
## BDI2.1 3.216181
## sex 1.302059
## age 1.190150
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.123578
## BDI2.1 3.933783
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.298571
## BDI2.1 3.528891
## sex 3.472110
## age 1.935791
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.403871
## BDI2.1 3.500228
## sex .
## age .
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.703140
## BDI2.1 2.744001
## sex 3.412656
## age 1.091501
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.423913
## BDI2.1 4.053464
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.829739
## BDI2.1 0.691695
## sex 2.064095
## age 1.862415
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.94569144
## BDI2.1 4.61416024
## sex .
## age -0.00178887
## running cv # 9
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.3291670
## BDI2.1 2.3685644
## sex 0.5409269
## age 0.9357658
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.159184
## BDI2.1 2.464825
## sex .
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.913406
## BDI2.1 2.309571
## sex 2.284906
## age 1.699350
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.450217
## BDI2.1 4.509916
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.648212
## BDI2.1 2.109075
## sex 3.259809
## age 1.552386
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4652991
## BDI2.1 4.6341301
## sex .
## age -0.1143948
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.815318
## BDI2.1 3.018786
## sex 1.540362
## age 2.125544
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.767906
## BDI2.1 4.207592
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 10.760772
## BDI2.1 3.033419
## sex 2.956071
## age 1.542836
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.701907
## BDI2.1 4.936906
## sex .
## age .
## running cv # 10
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.874770
## BDI2.1 2.973325
## sex 3.422192
## age 1.980329
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.4026125
## BDI2.1 3.6613985
## sex 0.6051038
## age -0.6449314
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.8862918
## BDI2.1 2.5549805
## sex 0.8753232
## age 1.3418445
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.407654
## BDI2.1 4.236733
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.61716665
## BDI2.1 2.88395091
## sex 0.02796956
## age 1.20462319
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.360642
## BDI2.1 4.922473
## sex 0.150807
## age .
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.8069614
## BDI2.1 0.5284736
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3672150
## BDI2.1 3.9170488
## sex .
## age -0.4568765
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.892975
## BDI2.1 1.766807
## sex 3.477384
## age 1.126407
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.76561
## BDI2.1 4.74826
## sex .
## age .mult.pred.m1.bdi = as.data.frame(mult.pred.m1.bdi)
mult.pred.m1.bdi$mean = rowMeans(cbind(mult.pred.m1.bdi$V1, mult.pred.m1.bdi$V2, mult.pred.m1.bdi$V3, mult.pred.m1.bdi$V4, mult.pred.m1.bdi$V5,
mult.pred.m1.bdi$V6, mult.pred.m1.bdi$V7, mult.pred.m1.bdi$V8, mult.pred.m1.bdi$V9, mult.pred.m1.bdi$V10))
describe(abs(mult.pred.m1.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 2.47 1.8 2.14 2.31 1.78 0.02 10.12 10.1 1.05 1.52
## se Q0.25 Q0.75
## 1 0.12 1.14 3.71mult.pred.m1.bdi = cbind(mult.pred.m1.bdi, bdi.pai)
mult.pred.m1.bdi$indication = if_else(mult.pred.m1.bdi$mean < 0, 0, 1)
# Analyses across tx group
mult.pred.m1.bdi$match = if_else(mult.pred.m1.bdi$indication==mult.pred.m1.bdi$txgrp, 0, 1) # Matched = 0
describeBy(mult.pred.m1.bdi$y, group = mult.pred.m1.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 100 15.44 10.01 14.5 14.79 11.12 0 46 46 0.55 -0.05 1
## Q0.25 Q0.75
## 1 7.75 22
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 108 16.95 12.33 13.5 15.97 11.12 0 51 51 0.72 -0.38
## se Q0.25 Q0.75
## 1 1.19 7 26.25t.test(mult.pred.m1.bdi$y ~ mult.pred.m1.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m1.bdi$y by mult.pred.m1.bdi$match
## t = -0.97855, df = 202.56, p-value = 0.329
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.579308 1.541597
## sample estimates:
## mean in group 0 mean in group 1
## 15.43509 16.95395cohen.d(mult.pred.m1.bdi$y ~ mult.pred.m1.bdi$match)##
## Cohen's d
##
## d estimate: -0.1347231 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.4086396 0.1391934#### Model 2 (FFM facets only)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(bdi.pai) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(bdi.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(bdi.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = bdi.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:30)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = bdi.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30)]
X_train_med_data = bdi.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 34)]
X_train_med_enfunc = X_train_med_data[,c(1:30)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = bdi.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30)]
bdi.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
bdi.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(bdi.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(bdi.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(bdi.cbt.model2, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(bdi.med.model2, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m2.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.76558301
## NRN1.1 1.44911459
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.44424235
## NRE2.1 .
## NRE3.1 0.51462238
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.48647366
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.09100341
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.95838413
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.03443095
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.721775
## NRN1.1 1.436593
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.446907
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.23347922
## NRN1.1 2.87257712
## NRN2.1 0.87138502
## NRN3.1 -1.56711287
## NRN4.1 0.18901263
## NRN5.1 0.42719903
## NRN6.1 -0.39054879
## NRE1.1 -1.74951333
## NRE2.1 1.69141918
## NRE3.1 1.00342379
## NRE4.1 -0.46069512
## NRE5.1 0.06341354
## NRE6.1 -1.60201061
## NRO1.1 1.21726915
## NRO2.1 -1.02459173
## NRO3.1 0.14102356
## NRO4.1 1.36824426
## NRO5.1 0.89018681
## NRO6.1 -1.78062145
## NRA1.1 -0.25308355
## NRA2.1 1.11952515
## NRA3.1 1.85098190
## NRA4.1 1.71956227
## NRA5.1 2.36001844
## NRA6.1 -2.03092063
## NRC1.1 0.96810156
## NRC2.1 0.79892396
## NRC3.1 -0.95327528
## NRC4.1 0.46123855
## NRC5.1 -0.79249844
## NRC6.1 -0.18841769
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.366296
## NRN1.1 1.061949
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.4079944
## NRN1.1 3.0508671
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5114856
## NRE2.1 .
## NRE3.1 0.2143884
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.5583674
## NRO1.1 1.3696266
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.7375328
## NRA3.1 0.4682839
## NRA4.1 0.9732863
## NRA5.1 1.0351964
## NRA6.1 -1.3600890
## NRC1.1 0.4837279
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.8191354
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.289784483
## NRN1.1 1.056357819
## NRN2.1 0.034371477
## NRN3.1 1.025262206
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.821187662
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.399619805
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.258891816
## NRC2.1 0.004769689
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.2672746
## NRN1.1 2.1377844
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.4078910
## NRN6.1 0.2821868
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1819554
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8666439
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.1583411
## NRA1.1 .
## NRA2.1 0.8194525
## NRA3.1 .
## NRA4.1 1.0369772
## NRA5.1 2.1383303
## NRA6.1 -0.7919061
## NRC1.1 0.8414440
## NRC2.1 0.1821247
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.331597285
## NRN1.1 0.098417869
## NRN2.1 .
## NRN3.1 1.196802728
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 -0.003344953
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9066370
## NRN1.1 2.2407820
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.5129712
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.1031172
## NRA1.1 .
## NRA2.1 0.6635045
## NRA3.1 .
## NRA4.1 1.4153484
## NRA5.1 1.4729512
## NRA6.1 -1.3167302
## NRC1.1 0.0799522
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.5029598
## NRC5.1 0.1218899
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.80563870
## NRN1.1 1.51250663
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.04629325
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 2
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.3370589
## NRN1.1 1.9527720
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.4767365
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.9276364
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.1736482
## NRA3.1 .
## NRA4.1 0.9758382
## NRA5.1 0.9785443
## NRA6.1 -0.4280457
## NRC1.1 0.4721860
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.8420621
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.5448081
## NRN1.1 0.9860648
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.9519191
## NRN1.1 1.8033004
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.1265384
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.5710704
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.5574955
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.0241469
## NRA1.1 .
## NRA2.1 0.6370596
## NRA3.1 .
## NRA4.1 0.2783952
## NRA5.1 1.5853629
## NRA6.1 -0.5356817
## NRC1.1 .
## NRC2.1 0.9212238
## NRC3.1 .
## NRC4.1 0.1167881
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7580259
## NRN1.1 1.1922060
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.3645151
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.23247222
## NRN1.1 1.73875205
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.05523383
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.26962598
## NRO1.1 1.81978923
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.06300046
## NRO5.1 0.70277341
## NRO6.1 0.02114056
## NRA1.1 .
## NRA2.1 0.45859784
## NRA3.1 .
## NRA4.1 1.06407754
## NRA5.1 2.66331032
## NRA6.1 -1.28273091
## NRC1.1 1.85669592
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.27162571
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9528722
## NRN1.1 0.9429107
## NRN2.1 .
## NRN3.1 0.8402100
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1411760
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.62141872
## NRN1.1 3.58693144
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.27786807
## NRE2.1 .
## NRE3.1 0.84861012
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.98316985
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.08903241
## NRO5.1 0.25365119
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.23317858
## NRA3.1 0.66375621
## NRA4.1 1.27205825
## NRA5.1 1.00963926
## NRA6.1 -1.80946606
## NRC1.1 .
## NRC2.1 0.07159460
## NRC3.1 .
## NRC4.1 0.11462982
## NRC5.1 0.82177510
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.556677
## NRN1.1 1.349023
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -1.041254
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.15059526
## NRN1.1 2.59391421
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.24505543
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.09270194
## NRA3.1 .
## NRA4.1 1.31147464
## NRA5.1 2.01902920
## NRA6.1 -1.89179459
## NRC1.1 .
## NRC2.1 1.17624562
## NRC3.1 .
## NRC4.1 0.68475561
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7732029
## NRN1.1 0.3322681
## NRN2.1 .
## NRN3.1 0.8764270
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 3
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.035141370
## NRN1.1 2.332055189
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.859582463
## NRO2.1 -0.145645916
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.005135307
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.151532290
## NRA3.1 .
## NRA4.1 0.606155463
## NRA5.1 1.751875948
## NRA6.1 -1.242295852
## NRC1.1 .
## NRC2.1 1.372455441
## NRC3.1 .
## NRC4.1 0.371185044
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.74949756
## NRN1.1 0.94581779
## NRN2.1 .
## NRN3.1 0.08950685
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.06780995
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.99625038
## NRN1.1 4.30069381
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.95890254
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.85604223
## NRE6.1 -0.38690161
## NRO1.1 1.82271791
## NRO2.1 .
## NRO3.1 0.49464812
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.04469415
## NRA2.1 0.29315019
## NRA3.1 0.56364847
## NRA4.1 1.24810755
## NRA5.1 2.97688772
## NRA6.1 -2.75990247
## NRC1.1 1.05937734
## NRC2.1 0.03346885
## NRC3.1 .
## NRC4.1 1.00748708
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.2553154
## NRN1.1 1.4979889
## NRN2.1 .
## NRN3.1 0.7695228
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.8127802
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.72770258
## NRN1.1 1.00837731
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.09901206
## NRN5.1 0.63201143
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.25055513
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.78972710
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.55638374
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.90515137
## NRA6.1 -1.05262904
## NRC1.1 0.81933344
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.51488753
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.0052153
## NRN1.1 1.0739713
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 0.9548251
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.16385607
## NRN1.1 1.73312605
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.01892136
## NRE2.1 .
## NRE3.1 1.07488135
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.97795985
## NRO2.1 -0.07836005
## NRO3.1 .
## NRO4.1 0.33728224
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.44611989
## NRA3.1 .
## NRA4.1 1.33570327
## NRA5.1 1.09451897
## NRA6.1 -0.99937830
## NRC1.1 .
## NRC2.1 0.37705734
## NRC3.1 .
## NRC4.1 0.60464852
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 18.46459246
## NRN1.1 1.57837984
## NRN2.1 .
## NRN3.1 0.59385144
## NRN4.1 0.85396856
## NRN5.1 -0.32181046
## NRN6.1 .
## NRE1.1 -1.49169352
## NRE2.1 .
## NRE3.1 2.96555564
## NRE4.1 -0.53723225
## NRE5.1 0.98601551
## NRE6.1 -2.04145430
## NRO1.1 .
## NRO2.1 -0.41702548
## NRO3.1 0.11839790
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.60906088
## NRA1.1 -0.53966473
## NRA2.1 .
## NRA3.1 1.02537275
## NRA4.1 -0.09420446
## NRA5.1 0.30349590
## NRA6.1 0.15134190
## NRC1.1 2.30633444
## NRC2.1 .
## NRC3.1 1.18169495
## NRC4.1 .
## NRC5.1 -2.77284541
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5009523
## NRN1.1 2.2157904
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5276180
## NRE2.1 .
## NRE3.1 0.3411900
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8643746
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.0812530
## NRA3.1 .
## NRA4.1 1.6625762
## NRA5.1 0.8010581
## NRA6.1 -0.4623433
## NRC1.1 0.5518373
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.3929204
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.430551
## NRN1.1 1.188664
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 4
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.07930294
## NRN1.1 2.34378921
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.27529517
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.71481510
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.09636438
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.18083265
## NRA3.1 0.10551569
## NRA4.1 1.44375538
## NRA5.1 1.01782147
## NRA6.1 -0.75566273
## NRC1.1 .
## NRC2.1 0.50100596
## NRC3.1 .
## NRC4.1 0.63671596
## NRC5.1 0.32109507
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.6210627
## NRN1.1 0.7986441
## NRN2.1 .
## NRN3.1 1.7506817
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 1.1853960
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.6542245
## NRA6.1 .
## NRC1.1 0.6562812
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.52889228
## NRN1.1 3.63693528
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.57332981
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.26722008
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.95738980
## NRA3.1 .
## NRA4.1 0.03892992
## NRA5.1 0.72060317
## NRA6.1 -1.63639955
## NRC1.1 .
## NRC2.1 0.21816215
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.0759386
## NRN1.1 0.4393171
## NRN2.1 .
## NRN3.1 0.8073474
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5343811
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9791808
## NRN1.1 1.9004945
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.1483306
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8877506
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.5151432
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.0103113
## NRA3.1 .
## NRA4.1 0.9996156
## NRA5.1 2.0823791
## NRA6.1 -1.1126275
## NRC1.1 0.7940517
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.0828393
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.986207
## NRN1.1 1.040788
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.53168866
## NRN1.1 2.72674920
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.01414237
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.44934473
## NRO1.1 1.46147873
## NRO2.1 -0.21682845
## NRO3.1 .
## NRO4.1 0.13246906
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.43145052
## NRA3.1 0.39039598
## NRA4.1 1.56061721
## NRA5.1 1.37432275
## NRA6.1 -1.63429868
## NRC1.1 0.47170517
## NRC2.1 0.58733221
## NRC3.1 .
## NRC4.1 0.16054756
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.535511
## NRN1.1 1.210154
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.13675656
## NRN1.1 1.51883026
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.82169088
## NRN6.1 .
## NRE1.1 -0.15904776
## NRE2.1 .
## NRE3.1 0.05603874
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.06050938
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.63132406
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.03012826
## NRA5.1 2.67842664
## NRA6.1 -1.13367387
## NRC1.1 1.22511276
## NRC2.1 1.23502489
## NRC3.1 -0.28413148
## NRC4.1 1.11224987
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.2951242
## NRN1.1 1.4454710
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.4023972
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 5
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.23037758
## NRN1.1 3.63045488
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.54137903
## NRE2.1 .
## NRE3.1 0.23995679
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.60607759
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.35194898
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.81354211
## NRA3.1 0.00438033
## NRA4.1 0.63696636
## NRA5.1 0.97729962
## NRA6.1 -1.64502926
## NRC1.1 0.49718734
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.62552352
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.6740021
## NRN1.1 0.9082951
## NRN2.1 .
## NRN3.1 0.2759422
## NRN4.1 1.0904049
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.3633458
## NRE2.1 .
## NRE3.1 1.1114982
## NRE4.1 -0.2767745
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.6708667
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.2141813
## NRA2.1 .
## NRA3.1 0.1764473
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.6440624
## NRC2.1 0.6219174
## NRC3.1 1.3273185
## NRC4.1 -0.2907528
## NRC5.1 -1.4469304
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.90103696
## NRN1.1 2.11350286
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.09066116
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.80153349
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.91357999
## NRA3.1 .
## NRA4.1 1.47710927
## NRA5.1 0.71705035
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.93406683
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4917865
## NRN1.1 2.5390949
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.8360560
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -1.0189520
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1559288
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -1.2722444
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.85843732
## NRN1.1 2.26251973
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.09607271
## NRN6.1 .
## NRE1.1 -0.13030602
## NRE2.1 0.60130795
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.66726102
## NRO2.1 -1.04589131
## NRO3.1 .
## NRO4.1 0.04114048
## NRO5.1 0.06913106
## NRO6.1 .
## NRA1.1 0.01191617
## NRA2.1 1.04056062
## NRA3.1 .
## NRA4.1 0.48858843
## NRA5.1 2.39475088
## NRA6.1 -1.20848656
## NRC1.1 1.50781404
## NRC2.1 0.02406788
## NRC3.1 -0.53492317
## NRC4.1 0.54898836
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.22051080
## NRN1.1 0.94361729
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.01731192
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.01680046
## NRN1.1 1.01256603
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.08356749
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.05421473
## NRA1.1 .
## NRA2.1 0.50196873
## NRA3.1 .
## NRA4.1 0.10186663
## NRA5.1 1.52429347
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.58471381
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.0993626
## NRN1.1 0.6052916
## NRN2.1 .
## NRN3.1 1.0717644
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2594125
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.43464624
## NRN1.1 2.10440210
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.05571114
## NRE2.1 .
## NRE3.1 0.40224422
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.87214989
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.41047500
## NRA3.1 .
## NRA4.1 1.11421863
## NRA5.1 1.13116637
## NRA6.1 -1.21428550
## NRC1.1 .
## NRC2.1 0.63897097
## NRC3.1 .
## NRC4.1 0.30152709
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.05843201
## NRN1.1 1.65197575
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.06418126
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 6
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.96065054
## NRN1.1 1.50656394
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.53925767
## NRE1.1 -0.97630652
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.20855623
## NRO1.1 1.71278129
## NRO2.1 -0.78576216
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.06856356
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.75279378
## NRA3.1 .
## NRA4.1 1.14312807
## NRA5.1 2.11574526
## NRA6.1 -0.46001265
## NRC1.1 0.15530225
## NRC2.1 0.46660094
## NRC3.1 .
## NRC4.1 1.05172899
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.56849857
## NRN1.1 1.74003759
## NRN2.1 .
## NRN3.1 1.21821609
## NRN4.1 .
## NRN5.1 -1.19000975
## NRN6.1 0.14746852
## NRE1.1 -2.95893590
## NRE2.1 0.07462286
## NRE3.1 2.59898964
## NRE4.1 -0.87063259
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.97499530
## NRO2.1 -0.44567800
## NRO3.1 0.01430957
## NRO4.1 -0.20854666
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.11458944
## NRA2.1 .
## NRA3.1 1.35084534
## NRA4.1 .
## NRA5.1 0.78716545
## NRA6.1 .
## NRC1.1 1.49667254
## NRC2.1 0.13808828
## NRC3.1 0.99941922
## NRC4.1 .
## NRC5.1 -3.06789788
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.24300799
## NRN1.1 2.40251728
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.29755634
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.29122247
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.52999684
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.78840364
## NRA3.1 .
## NRA4.1 1.71362960
## NRA5.1 1.85647337
## NRA6.1 -1.47625351
## NRC1.1 0.01795659
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.36520337
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.927625071
## NRN1.1 0.920806681
## NRN2.1 .
## NRN3.1 1.813474388
## NRN4.1 0.008543589
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -2.010958102
## NRE2.1 .
## NRE3.1 2.172670483
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.525194067
## NRA2.1 0.604394844
## NRA3.1 1.722287272
## NRA4.1 -2.060444915
## NRA5.1 .
## NRA6.1 0.445069687
## NRC1.1 3.205312379
## NRC2.1 1.027819731
## NRC3.1 0.125892300
## NRC4.1 .
## NRC5.1 -4.139926406
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.57122682
## NRN1.1 4.34172543
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.31884984
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.66066730
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.52717206
## NRO5.1 0.45626884
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.15186716
## NRA3.1 1.27689567
## NRA4.1 1.22002063
## NRA5.1 0.85485082
## NRA6.1 -3.14769393
## NRC1.1 0.04345929
## NRC2.1 0.63151618
## NRC3.1 .
## NRC4.1 0.03529873
## NRC5.1 0.28087355
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.420269
## NRN1.1 1.542315
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.46827280
## NRN1.1 2.12730314
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.32250718
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.60446359
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.78098994
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.81496199
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.01574737
## NRA3.1 .
## NRA4.1 .
## NRA5.1 2.63784758
## NRA6.1 -1.66545320
## NRC1.1 1.74625802
## NRC2.1 0.11167294
## NRC3.1 -0.20097487
## NRC4.1 1.11921885
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.835829
## NRN1.1 1.614083
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.99098130
## NRN1.1 2.33628608
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.07352564
## NRE4.1 0.03929946
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.19162290
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.70702778
## NRA3.1 .
## NRA4.1 0.64329695
## NRA5.1 1.34358139
## NRA6.1 -0.59932085
## NRC1.1 0.59639515
## NRC2.1 0.58445135
## NRC3.1 .
## NRC4.1 0.65348011
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.018097431
## NRN1.1 1.452750076
## NRN2.1 .
## NRN3.1 1.414984790
## NRN4.1 0.111648249
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.003236196
## NRE3.1 2.045923302
## NRE4.1 .
## NRE5.1 1.326319309
## NRE6.1 -2.229662022
## NRO1.1 .
## NRO2.1 .
## NRO3.1 -0.108513005
## NRO4.1 0.357948540
## NRO5.1 -0.197328911
## NRO6.1 .
## NRA1.1 -0.784243713
## NRA2.1 0.572380534
## NRA3.1 1.456618902
## NRA4.1 .
## NRA5.1 0.927284668
## NRA6.1 .
## NRC1.1 1.753994845
## NRC2.1 .
## NRC3.1 0.642041093
## NRC4.1 .
## NRC5.1 -1.424028227
## NRC6.1 .
## running cv # 7
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.98708758
## NRN1.1 1.54419527
## NRN2.1 .
## NRN3.1 0.02235089
## NRN4.1 .
## NRN5.1 0.41958827
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.44147304
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.58388773
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.39644383
## NRO5.1 0.04451375
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.59208793
## NRA3.1 .
## NRA4.1 0.06479636
## NRA5.1 2.50895652
## NRA6.1 -1.28709395
## NRC1.1 0.61276153
## NRC2.1 1.17579180
## NRC3.1 .
## NRC4.1 0.18756017
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.9752997
## NRN1.1 1.1296547
## NRN2.1 .
## NRN3.1 0.7852355
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.51577311
## NRN1.1 2.65813504
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.05245623
## NRO1.1 0.95508926
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.19412396
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.90806076
## NRA3.1 .
## NRA4.1 1.39293819
## NRA5.1 1.82433220
## NRA6.1 -1.78151248
## NRC1.1 0.13293341
## NRC2.1 0.96428625
## NRC3.1 .
## NRC4.1 0.56581844
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9654376
## NRN1.1 1.7197490
## NRN2.1 .
## NRN3.1 0.2949560
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.2677966
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.03688532
## NRN1.1 2.82812302
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.11609864
## NRE1.1 -1.56731295
## NRE2.1 0.83036387
## NRE3.1 0.73124618
## NRE4.1 1.06250758
## NRE5.1 -0.37260457
## NRE6.1 -0.46847356
## NRO1.1 1.70087143
## NRO2.1 -0.74276049
## NRO3.1 .
## NRO4.1 0.08406102
## NRO5.1 .
## NRO6.1 -0.15891229
## NRA1.1 .
## NRA2.1 0.86998646
## NRA3.1 0.77406012
## NRA4.1 1.71182472
## NRA5.1 1.10269625
## NRA6.1 -0.89943103
## NRC1.1 0.47682053
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.55577001
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5537134
## NRN1.1 0.4542757
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.9710428
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.6921105
## NRN1.1 1.9219992
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.3224326
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.6454812
## NRA1.1 .
## NRA2.1 0.8648662
## NRA3.1 .
## NRA4.1 0.8292653
## NRA5.1 1.9277579
## NRA6.1 -0.8894505
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.2932663
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.191535
## NRN1.1 1.481289
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 1.020733
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.54037663
## NRN1.1 1.97212446
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.86066571
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.82134333
## NRA3.1 0.01892007
## NRA4.1 0.43212754
## NRA5.1 0.38479968
## NRA6.1 -0.54018608
## NRC1.1 0.40891913
## NRC2.1 0.37774668
## NRC3.1 .
## NRC4.1 0.13343854
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.82547032
## NRN1.1 0.85108182
## NRN2.1 .
## NRN3.1 2.27880639
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.33445727
## NRE2.1 .
## NRE3.1 1.24253246
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.92292189
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.38000598
## NRA6.1 .
## NRC1.1 3.18581969
## NRC2.1 0.04046341
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -1.90162398
## NRC6.1 .
## running cv # 8
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.7194175
## NRN1.1 1.5767221
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.4815271
## NRN6.1 .
## NRE1.1 -0.7365008
## NRE2.1 .
## NRE3.1 0.3070617
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.4359016
## NRO2.1 -0.1336477
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.7357578
## NRA3.1 0.1665009
## NRA4.1 1.3194072
## NRA5.1 0.9641778
## NRA6.1 .
## NRC1.1 .
## NRC2.1 0.5477025
## NRC3.1 .
## NRC4.1 0.5891754
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8163895
## NRN1.1 0.5414534
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.877337251
## NRN1.1 3.124532464
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.030845627
## NRE2.1 .
## NRE3.1 0.323040164
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.914873656
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.006198148
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.879238918
## NRA3.1 0.105701695
## NRA4.1 0.553181149
## NRA5.1 0.974390740
## NRA6.1 -1.560585867
## NRC1.1 0.021889438
## NRC2.1 0.434102650
## NRC3.1 .
## NRC4.1 0.632624095
## NRC5.1 0.124715358
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.23819595
## NRN1.1 1.29774744
## NRN2.1 .
## NRN3.1 1.07864892
## NRN4.1 .
## NRN5.1 -0.70164133
## NRN6.1 .
## NRE1.1 -1.60188284
## NRE2.1 .
## NRE3.1 3.52008432
## NRE4.1 -0.09049618
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 -0.03520478
## NRO6.1 .
## NRA1.1 -1.42786541
## NRA2.1 .
## NRA3.1 1.56269906
## NRA4.1 .
## NRA5.1 0.47864156
## NRA6.1 .
## NRC1.1 0.30029773
## NRC2.1 0.06398257
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -2.57970067
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.59949951
## NRN1.1 2.50854788
## NRN2.1 0.01295659
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.21055822
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.03873843
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.30965000
## NRO5.1 .
## NRO6.1 0.07235729
## NRA1.1 .
## NRA2.1 0.63198860
## NRA3.1 .
## NRA4.1 1.16985875
## NRA5.1 1.95510836
## NRA6.1 -1.17829544
## NRC1.1 0.55893323
## NRC2.1 0.61922087
## NRC3.1 .
## NRC4.1 0.54602562
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9875914
## NRN1.1 1.9902584
## NRN2.1 0.5123869
## NRN3.1 1.5259641
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.2830367
## NRE2.1 .
## NRE3.1 1.5766687
## NRE4.1 -0.5384375
## NRE5.1 0.1297511
## NRE6.1 .
## NRO1.1 0.2839082
## NRO2.1 0.1584356
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.9065074
## NRC2.1 0.7575098
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -1.9376336
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1804634
## NRN1.1 2.0304101
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.0799989
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.9794968
## NRA6.1 -1.3275443
## NRC1.1 1.1311383
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.1502696
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.6619669
## NRN1.1 1.3540126
## NRN2.1 .
## NRN3.1 0.6807192
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.04054083
## NRN1.1 1.49030342
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.02923526
## NRE3.1 .
## NRE4.1 0.55097624
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.63491666
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.01827294
## NRA3.1 .
## NRA4.1 1.21962477
## NRA5.1 1.35185281
## NRA6.1 -0.83003859
## NRC1.1 .
## NRC2.1 0.05689535
## NRC3.1 .
## NRC4.1 0.42120112
## NRC5.1 0.50715729
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.58615324
## NRN1.1 1.86735166
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 0.02071626
## NRC4.1 .
## NRC5.1 -0.29508059
## NRC6.1 .
## running cv # 9
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.22960978
## NRN1.1 1.84154149
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.85804131
## NRO2.1 -0.59468232
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.25218264
## NRA3.1 .
## NRA4.1 0.08121878
## NRA5.1 2.55992637
## NRA6.1 -0.31221571
## NRC1.1 1.92626667
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.51020446
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.60919981
## NRN1.1 2.40485560
## NRN2.1 1.05776481
## NRN3.1 1.63516124
## NRN4.1 .
## NRN5.1 -1.63125403
## NRN6.1 .
## NRE1.1 -2.99038949
## NRE2.1 .
## NRE3.1 2.13536567
## NRE4.1 .
## NRE5.1 0.01384174
## NRE6.1 0.96079347
## NRO1.1 .
## NRO2.1 -0.78135435
## NRO3.1 1.06786751
## NRO4.1 -0.01218870
## NRO5.1 -1.08745916
## NRO6.1 .
## NRA1.1 .
## NRA2.1 -0.80478193
## NRA3.1 1.22044933
## NRA4.1 -1.02344091
## NRA5.1 .
## NRA6.1 0.95376225
## NRC1.1 3.96950805
## NRC2.1 3.31119340
## NRC3.1 1.66237071
## NRC4.1 1.20197496
## NRC5.1 -6.43211703
## NRC6.1 -1.55405650
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5508230
## NRN1.1 2.3713835
## NRN2.1 1.0891554
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.5864835
## NRE4.1 0.2471826
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.0337221
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.1125436
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.1084242
## NRA3.1 0.1969869
## NRA4.1 0.9800731
## NRA5.1 1.5634549
## NRA6.1 -1.8993866
## NRC1.1 0.3634571
## NRC2.1 0.5124028
## NRC3.1 .
## NRC4.1 0.2767789
## NRC5.1 0.3001565
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4897053
## NRN1.1 0.8168315
## NRN2.1 .
## NRN3.1 0.9886613
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.3959597
## NRN1.1 1.7519212
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 1.0292504
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.3254395
## NRA3.1 .
## NRA4.1 1.0079767
## NRA5.1 2.6292979
## NRA6.1 -0.2349567
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.737293
## NRN1.1 1.084062
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.8122701
## NRN1.1 3.1803714
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.3638762
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.2254419
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.6139388
## NRA3.1 .
## NRA4.1 1.6925423
## NRA5.1 0.5677729
## NRA6.1 -1.5735893
## NRC1.1 .
## NRC2.1 0.5525854
## NRC3.1 .
## NRC4.1 0.3093798
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.31741692
## NRN1.1 0.96024160
## NRN2.1 .
## NRN3.1 0.58524227
## NRN4.1 .
## NRN5.1 -0.94802079
## NRN6.1 .
## NRE1.1 -2.14953604
## NRE2.1 .
## NRE3.1 1.66815305
## NRE4.1 -2.23460849
## NRE5.1 1.12567975
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.02026308
## NRO5.1 .
## NRO6.1 -0.47344449
## NRA1.1 -0.75667249
## NRA2.1 1.23777377
## NRA3.1 2.27546950
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.32986321
## NRC2.1 1.01495593
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -2.06986492
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.4960888
## NRN1.1 2.1302934
## NRN2.1 0.3730112
## NRN3.1 -0.4844566
## NRN4.1 0.1737221
## NRN5.1 0.6532937
## NRN6.1 1.3007619
## NRE1.1 -1.3899492
## NRE2.1 0.4772525
## NRE3.1 0.9008656
## NRE4.1 0.3252570
## NRE5.1 0.8628495
## NRE6.1 -1.8804435
## NRO1.1 2.2178315
## NRO2.1 -1.2577443
## NRO3.1 -0.2976115
## NRO4.1 -0.2096656
## NRO5.1 -0.5515674
## NRO6.1 0.3672379
## NRA1.1 -0.5180169
## NRA2.1 0.9534845
## NRA3.1 1.0780778
## NRA4.1 1.5213318
## NRA5.1 1.6990568
## NRA6.1 -1.8281673
## NRC1.1 2.1475345
## NRC2.1 1.2276290
## NRC3.1 -1.7131576
## NRC4.1 1.6674318
## NRC5.1 -1.2334327
## NRC6.1 -0.3740170
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.43715
## NRN1.1 1.30123
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 10
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1421973
## NRN1.1 2.3523177
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.1263564
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.3809949
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.6479128
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.1187691
## NRO5.1 .
## NRO6.1 0.2983255
## NRA1.1 .
## NRA2.1 0.8774076
## NRA3.1 .
## NRA4.1 0.6435423
## NRA5.1 1.8714315
## NRA6.1 -1.1638199
## NRC1.1 0.2612131
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.5845208
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.26611
## NRN1.1 1.91497
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.53457450
## NRN1.1 2.75177293
## NRN2.1 0.20011924
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.02743665
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.21534335
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.51946136
## NRA3.1 .
## NRA4.1 0.98254709
## NRA5.1 1.04286336
## NRA6.1 -0.95162825
## NRC1.1 .
## NRC2.1 0.93475306
## NRC3.1 .
## NRC4.1 0.55356029
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.31103167
## NRN1.1 0.55175610
## NRN2.1 .
## NRN3.1 0.10941773
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.05057179
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.63736153
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.433626888
## NRN1.1 2.035420554
## NRN2.1 0.299816604
## NRN3.1 -0.524741357
## NRN4.1 0.291954748
## NRN5.1 0.857613215
## NRN6.1 -0.762051501
## NRE1.1 -1.672294945
## NRE2.1 1.519599700
## NRE3.1 0.935414224
## NRE4.1 -0.584497553
## NRE5.1 0.482803589
## NRE6.1 -1.074666954
## NRO1.1 1.191951903
## NRO2.1 -1.437887250
## NRO3.1 0.013369443
## NRO4.1 1.127275370
## NRO5.1 1.296706504
## NRO6.1 -1.067059567
## NRA1.1 -0.151376695
## NRA2.1 1.452917321
## NRA3.1 1.369971329
## NRA4.1 0.033021095
## NRA5.1 2.844448070
## NRA6.1 -1.242565837
## NRC1.1 0.852406280
## NRC2.1 1.129017600
## NRC3.1 -0.831790922
## NRC4.1 0.437130714
## NRC5.1 -0.003911041
## NRC6.1 -0.272651391
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.93270119
## NRN1.1 0.86402175
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.03147322
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.7868176
## NRN1.1 2.4515259
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.2750529
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.4982441
## NRA3.1 .
## NRA4.1 1.3310268
## NRA5.1 1.7675494
## NRA6.1 -1.5675454
## NRC1.1 0.8343053
## NRC2.1 0.2072966
## NRC3.1 .
## NRC4.1 0.3921900
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3533467
## NRN1.1 1.1052871
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.3218037
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5874327
## NRN1.1 2.5669236
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.2219437
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.3243622
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.8555657
## NRA3.1 .
## NRA4.1 1.7463959
## NRA5.1 1.2732237
## NRA6.1 -1.0477744
## NRC1.1 1.3010363
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.9803155
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.55724747
## NRN1.1 0.57036360
## NRN2.1 .
## NRN3.1 1.08652976
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.03005363
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .mult.pred.m2.bdi = as.data.frame(mult.pred.m2.bdi)
mult.pred.m2.bdi$mean = rowMeans(cbind(mult.pred.m2.bdi$V1, mult.pred.m2.bdi$V2, mult.pred.m2.bdi$V3, mult.pred.m2.bdi$V4, mult.pred.m2.bdi$V5,
mult.pred.m2.bdi$V6, mult.pred.m2.bdi$V7, mult.pred.m2.bdi$V8, mult.pred.m2.bdi$V9, mult.pred.m2.bdi$V10))
describe(abs(mult.pred.m2.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 3.34 2.51 2.8 3.09 2.53 0.01 12.52 12.51 0.95 0.69
## se Q0.25 Q0.75
## 1 0.17 1.41 4.82mult.pred.m2.bdi = cbind(mult.pred.m2.bdi, bdi.pai)
mult.pred.m2.bdi$indication = if_else(mult.pred.m2.bdi$mean < 0, 0, 1)
# Analyses across tx group
mult.pred.m2.bdi$match = if_else(mult.pred.m2.bdi$indication==mult.pred.m2.bdi$txgrp, 0, 1)
describeBy(mult.pred.m2.bdi$y, group = mult.pred.m2.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 102 15.27 10.99 14 14.3 10.38 0 47 47 0.74 0.15
## se Q0.25 Q0.75
## 1 1.09 7 21
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 106 17.14 11.52 14 16.25 11.86 0 51 51 0.67 -0.32
## se Q0.25 Q0.75
## 1 1.12 8.25 25.75t.test(mult.pred.m2.bdi$y ~ mult.pred.m2.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m2.bdi$y by mult.pred.m2.bdi$match
## t = -1.1973, df = 205.99, p-value = 0.2326
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.944770 1.208206
## sample estimates:
## mean in group 0 mean in group 1
## 15.27162 17.13990cohen.d(mult.pred.m2.bdi$y ~ mult.pred.m2.bdi$match)##
## Cohen's d
##
## d estimate: -0.1659129 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.4398377 0.1080119#### Model 3 (Demographics and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(bdi.pai) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(bdi.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(bdi.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = bdi.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 32:34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:30, 31, 32)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = bdi.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30, 32, 33)]
X_train_med_data = bdi.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 32:34)]
X_train_med_enfunc = X_train_med_data[,c(1:30, 31, 32)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = bdi.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30, 32, 33)]
bdi.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
bdi.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(bdi.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(bdi.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(bdi.cbt.model3, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(bdi.med.model3, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m3.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.76558301
## NRN1.1 1.44911459
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.44424235
## NRE2.1 .
## NRE3.1 0.51462238
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.48647366
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.09100341
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.95838413
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.03443095
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7211420
## NRN1.1 1.3859152
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.3971667
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.88714055
## NRN1.1 3.00307714
## NRN2.1 0.59723921
## NRN3.1 -1.70403108
## NRN4.1 0.32949305
## NRN5.1 0.65213633
## NRN6.1 -0.29907594
## NRE1.1 -1.71384661
## NRE2.1 1.56884691
## NRE3.1 0.93093215
## NRE4.1 -0.47535252
## NRE5.1 0.39173044
## NRE6.1 -1.68921423
## NRO1.1 1.36544149
## NRO2.1 -0.98858137
## NRO3.1 0.35255503
## NRO4.1 1.25788700
## NRO5.1 0.85335323
## NRO6.1 -1.71673280
## NRA1.1 -0.31095151
## NRA2.1 0.85751937
## NRA3.1 1.78374119
## NRA4.1 1.56241776
## NRA5.1 2.44026418
## NRA6.1 -2.05821660
## NRC1.1 1.00053633
## NRC2.1 0.84118503
## NRC3.1 -1.12688322
## NRC4.1 0.49076377
## NRC5.1 -0.65440906
## NRC6.1 -0.09008668
## sex -0.42840843
## age 1.07344491
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.366296
## NRN1.1 1.061949
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.4079944
## NRN1.1 3.0508671
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5114856
## NRE2.1 .
## NRE3.1 0.2143884
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.5583674
## NRO1.1 1.3696266
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.7375328
## NRA3.1 0.4682839
## NRA4.1 0.9732863
## NRA5.1 1.0351964
## NRA6.1 -1.3600890
## NRC1.1 0.4837279
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.8191354
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.2561464
## NRN1.1 0.9018371
## NRN2.1 .
## NRN3.1 0.3999472
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2268644
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.7814897
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.29335664
## NRN1.1 2.08034563
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.41279461
## NRN6.1 0.33725197
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.05863855
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.97032067
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.13647758
## NRA1.1 .
## NRA2.1 0.67164038
## NRA3.1 .
## NRA4.1 0.99098029
## NRA5.1 2.11735036
## NRA6.1 -0.82988525
## NRC1.1 0.85277900
## NRC2.1 0.16913374
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.50204783
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.331597285
## NRN1.1 0.098417869
## NRN2.1 .
## NRN3.1 1.196802728
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 -0.003344953
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.91019422
## NRN1.1 2.22798481
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.55444573
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.08139059
## NRA1.1 .
## NRA2.1 0.56993787
## NRA3.1 .
## NRA4.1 1.40072534
## NRA5.1 1.49854349
## NRA6.1 -1.32726942
## NRC1.1 0.07575600
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.50235944
## NRC5.1 0.11457475
## NRC6.1 .
## sex .
## age 0.23479835
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.80563870
## NRN1.1 1.51250663
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.04629325
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running cv # 2
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.28447157
## NRN1.1 1.91493450
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.44818265
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.90773322
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.14364023
## NRA3.1 .
## NRA4.1 0.96696976
## NRA5.1 0.95677877
## NRA6.1 -0.40174416
## NRC1.1 0.44421290
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.82439375
## NRC5.1 .
## NRC6.1 .
## sex 0.02896403
## age 0.06231963
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.5294379
## NRN1.1 0.9496914
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age -0.3567232
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.937116450
## NRN1.1 1.707875793
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.443563013
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.416590165
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.581327539
## NRA3.1 .
## NRA4.1 0.102672960
## NRA5.1 1.392104301
## NRA6.1 -0.233286623
## NRC1.1 .
## NRC2.1 0.900434212
## NRC3.1 .
## NRC4.1 0.007874649
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7580259
## NRN1.1 1.1922060
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.3645151
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.23514158
## NRN1.1 1.72113163
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.02757547
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.20956713
## NRO1.1 1.77317259
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.04052160
## NRO5.1 0.68381178
## NRO6.1 0.05039929
## NRA1.1 .
## NRA2.1 0.46475037
## NRA3.1 .
## NRA4.1 1.04731043
## NRA5.1 2.62186169
## NRA6.1 -1.25881545
## NRC1.1 1.82303705
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.24528329
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9528722
## NRN1.1 0.9429107
## NRN2.1 .
## NRN3.1 0.8402100
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1411760
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.98491866
## NRN1.1 3.66449520
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.36405492
## NRE2.1 .
## NRE3.1 0.88539051
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.03346550
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.14301380
## NRO5.1 0.28340388
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.17685311
## NRA3.1 0.78387053
## NRA4.1 1.28704763
## NRA5.1 1.10938819
## NRA6.1 -1.90458923
## NRC1.1 .
## NRC2.1 0.07784718
## NRC3.1 .
## NRC4.1 0.11432209
## NRC5.1 0.81285001
## NRC6.1 .
## sex -0.23061208
## age 0.05556594
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.556677
## NRN1.1 1.349023
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -1.041254
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1347928
## NRN1.1 2.6366918
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.3378272
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 1.2899265
## NRA5.1 2.0462604
## NRA6.1 -1.9756329
## NRC1.1 .
## NRC2.1 1.1757031
## NRC3.1 .
## NRC4.1 0.6832834
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.3568742
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7732029
## NRN1.1 0.3322681
## NRN2.1 .
## NRN3.1 0.8764270
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running cv # 3
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.0497750
## NRN1.1 2.2621138
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.6837577
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.1207910
## NRA3.1 .
## NRA4.1 0.5242398
## NRA5.1 1.6063565
## NRA6.1 -1.1300463
## NRC1.1 .
## NRC2.1 1.3243259
## NRC3.1 .
## NRC4.1 0.1963976
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.74949756
## NRN1.1 0.94581779
## NRN2.1 .
## NRN3.1 0.08950685
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.06780995
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.99625039
## NRN1.1 4.30069384
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.95890270
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.85604220
## NRE6.1 -0.38690155
## NRO1.1 1.82271789
## NRO2.1 .
## NRO3.1 0.49464814
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.04469410
## NRA2.1 0.29315013
## NRA3.1 0.56364854
## NRA4.1 1.24810756
## NRA5.1 2.97688773
## NRA6.1 -2.75990248
## NRC1.1 1.05937736
## NRC2.1 0.03346877
## NRC3.1 .
## NRC4.1 1.00748709
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.2553154
## NRN1.1 1.4979889
## NRN2.1 .
## NRN3.1 0.7695228
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.8127802
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.7277510450
## NRN1.1 1.0083773099
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.0990120574
## NRN5.1 0.6320114348
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.2505551259
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.7897271028
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.5563837386
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.9051513659
## NRA6.1 -1.0526290438
## NRC1.1 0.8193334356
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.5148875309
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.0008949061
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9966954
## NRN1.1 0.9832029
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 0.8614443
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1807100
## NRN1.1 1.7168811
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.9334863
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.1399872
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.2836854
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.1434629
## NRA3.1 .
## NRA4.1 1.2802294
## NRA5.1 1.1051998
## NRA6.1 -1.0614635
## NRC1.1 .
## NRC2.1 0.3812985
## NRC3.1 .
## NRC4.1 0.6086743
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.8352278
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3426857411
## NRN1.1 1.5569532709
## NRN2.1 .
## NRN3.1 0.5599353101
## NRN4.1 0.8373682466
## NRN5.1 -0.1724726794
## NRN6.1 .
## NRE1.1 -1.2879220844
## NRE2.1 .
## NRE3.1 2.8781875867
## NRE4.1 -0.5230878128
## NRE5.1 0.7007962686
## NRE6.1 -2.0299889844
## NRO1.1 .
## NRO2.1 -0.2622640147
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.5474984433
## NRA1.1 -0.2036890589
## NRA2.1 .
## NRA3.1 0.9298548642
## NRA4.1 .
## NRA5.1 0.2165256303
## NRA6.1 0.0008989984
## NRC1.1 2.0368474861
## NRC2.1 .
## NRC3.1 1.3671375896
## NRC4.1 .
## NRC5.1 -2.7259199743
## NRC6.1 .
## sex 0.6946461116
## age -0.8167163213
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.4928908
## NRN1.1 2.2006540
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5217969
## NRE2.1 .
## NRE3.1 0.2706327
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.9965735
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.9061014
## NRA3.1 .
## NRA4.1 1.6257601
## NRA5.1 0.8173416
## NRA6.1 -0.4627904
## NRC1.1 0.5501932
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.4022925
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.4213449
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.430551
## NRN1.1 1.188664
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running cv # 4
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.07276668
## NRN1.1 2.31520238
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.26710773
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.69560673
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.08524571
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.18490791
## NRA3.1 0.08719467
## NRA4.1 1.43876150
## NRA5.1 0.99421627
## NRA6.1 -0.71403442
## NRC1.1 .
## NRC2.1 0.48861265
## NRC3.1 .
## NRC4.1 0.63106710
## NRC5.1 0.32483284
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.6145422
## NRN1.1 0.4425909
## NRN2.1 .
## NRN3.1 1.0435987
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.3847310
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.1077452
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.54560315
## NRN1.1 3.64060212
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.52573563
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.33573501
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.79970234
## NRA3.1 .
## NRA4.1 0.03256686
## NRA5.1 0.74177039
## NRA6.1 -1.63616151
## NRC1.1 .
## NRC2.1 0.22821985
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.28836747
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.0759386
## NRN1.1 0.4393171
## NRN2.1 .
## NRN3.1 0.8073474
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5343811
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9882525
## NRN1.1 1.8955257
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.1350414
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.9369461
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.4916670
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.9200601
## NRA3.1 .
## NRA4.1 0.9827436
## NRA5.1 2.0978911
## NRA6.1 -1.1271099
## NRC1.1 0.7850335
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.0728999
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.2381790
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.98679832
## NRN1.1 1.03961633
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age -0.02223151
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.55192092
## NRN1.1 2.68003623
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.45743279
## NRO1.1 1.51124784
## NRO2.1 -0.12776751
## NRO3.1 .
## NRO4.1 0.06609564
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.30745281
## NRA3.1 0.39354670
## NRA4.1 1.56693076
## NRA5.1 1.34885927
## NRA6.1 -1.71253118
## NRC1.1 0.45807246
## NRC2.1 0.59739928
## NRC3.1 .
## NRC4.1 0.12560011
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.37274886
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.535511
## NRN1.1 1.210154
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.13675656
## NRN1.1 1.51883026
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.82169088
## NRN6.1 .
## NRE1.1 -0.15904776
## NRE2.1 .
## NRE3.1 0.05603874
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.06050938
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.63132406
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.03012826
## NRA5.1 2.67842664
## NRA6.1 -1.13367387
## NRC1.1 1.22511276
## NRC2.1 1.23502489
## NRC3.1 -0.28413148
## NRC4.1 1.11224987
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.2951242
## NRN1.1 1.4454710
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.4023972
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running cv # 5
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.23037758
## NRN1.1 3.63045488
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.54137903
## NRE2.1 .
## NRE3.1 0.23995679
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.60607759
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.35194898
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.81354211
## NRA3.1 0.00438033
## NRA4.1 0.63696636
## NRA5.1 0.97729962
## NRA6.1 -1.64502926
## NRC1.1 0.49718734
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.62552352
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 11.03567137
## NRN1.1 0.89481362
## NRN2.1 .
## NRN3.1 0.33386257
## NRN4.1 1.37722332
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -1.03652267
## NRE2.1 .
## NRE3.1 1.96388785
## NRE4.1 -0.28892794
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.90626739
## NRO5.1 .
## NRO6.1 -0.02100209
## NRA1.1 -1.58842717
## NRA2.1 .
## NRA3.1 0.53377264
## NRA4.1 -0.06812920
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.32821507
## NRC2.1 0.82159116
## NRC3.1 1.80107414
## NRC4.1 -0.81279715
## NRC5.1 -2.40995121
## NRC6.1 .
## sex 2.91022993
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.90103696
## NRN1.1 2.11350286
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.09066116
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.80153349
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.91357999
## NRA3.1 .
## NRA4.1 1.47710927
## NRA5.1 0.71705035
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.93406683
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4921060
## NRN1.1 2.4117559
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.6812900
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.8585216
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -1.0789459
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 18.16796042
## NRN1.1 2.69961567
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.11848076
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.70037180
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.67894436
## NRO2.1 -1.34304041
## NRO3.1 .
## NRO4.1 0.15194813
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.10354492
## NRA3.1 .
## NRA4.1 0.05737006
## NRA5.1 3.32786929
## NRA6.1 -1.31122980
## NRC1.1 2.15371969
## NRC2.1 .
## NRC3.1 -1.04866573
## NRC4.1 0.55680421
## NRC5.1 .
## NRC6.1 .
## sex -2.08189184
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.20963796
## NRN1.1 0.91243702
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.01499041
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age -0.22837527
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 12.18026255
## NRN1.1 0.84952921
## NRN2.1 1.01006832
## NRN3.1 -1.02735045
## NRN4.1 0.78606326
## NRN5.1 1.09376027
## NRN6.1 0.27551937
## NRE1.1 -1.67913981
## NRE2.1 0.85313550
## NRE3.1 0.28149495
## NRE4.1 0.45570721
## NRE5.1 1.14054468
## NRE6.1 -1.91777720
## NRO1.1 1.65727087
## NRO2.1 -0.48162906
## NRO3.1 -0.03127938
## NRO4.1 0.29708872
## NRO5.1 0.65855751
## NRO6.1 -0.30240418
## NRA1.1 0.02780181
## NRA2.1 0.33318378
## NRA3.1 0.89236693
## NRA4.1 1.41076226
## NRA5.1 3.90407039
## NRA6.1 -1.85521232
## NRC1.1 2.29573608
## NRC2.1 1.72528361
## NRC3.1 -1.45788099
## NRC4.1 1.10572429
## NRC5.1 -0.96720030
## NRC6.1 -0.32645293
## sex 1.98161297
## age 2.03551351
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.0993626
## NRN1.1 0.6052916
## NRN2.1 .
## NRN3.1 1.0717644
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2594125
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.4345382
## NRN1.1 2.2076848
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2505056
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.0373258
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.1484777
## NRA3.1 .
## NRA4.1 1.0467142
## NRA5.1 1.1145393
## NRA6.1 -1.3593476
## NRC1.1 .
## NRC2.1 0.6567596
## NRC3.1 .
## NRC4.1 0.2292032
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.7819165
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.05843201
## NRN1.1 1.65197575
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.06418126
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running cv # 6
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.98748643
## NRN1.1 1.43368746
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.55124799
## NRE1.1 -0.86192873
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.07019963
## NRO1.1 1.53917250
## NRO2.1 -0.62152941
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.65793073
## NRA3.1 .
## NRA4.1 1.07919893
## NRA5.1 1.94944113
## NRA6.1 -0.51797880
## NRC1.1 0.01919775
## NRC2.1 0.44624735
## NRC3.1 .
## NRC4.1 0.92305399
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.34960644
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.45949926
## NRN1.1 1.85382171
## NRN2.1 .
## NRN3.1 1.20228700
## NRN4.1 .
## NRN5.1 -1.06558680
## NRN6.1 .
## NRE1.1 -3.36762022
## NRE2.1 .
## NRE3.1 2.69463339
## NRE4.1 -0.87256596
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.66810556
## NRO2.1 -0.22222388
## NRO3.1 .
## NRO4.1 -0.01514834
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.67813148
## NRA2.1 .
## NRA3.1 1.40577698
## NRA4.1 .
## NRA5.1 0.51088585
## NRA6.1 .
## NRC1.1 1.21996514
## NRC2.1 0.08763103
## NRC3.1 0.84412100
## NRC4.1 .
## NRC5.1 -3.28018340
## NRC6.1 .
## sex 1.24761114
## age -0.55706759
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.2424371
## NRN1.1 2.3567189
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2590556
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.2532134
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.5016043
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.7990916
## NRA3.1 .
## NRA4.1 1.6926498
## NRA5.1 1.7867165
## NRA6.1 -1.4111169
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.3669160
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9800500
## NRN1.1 0.9605905
## NRN2.1 .
## NRN3.1 1.8255068
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -1.9839184
## NRE2.1 .
## NRE3.1 2.1396642
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.1303542
## NRA2.1 0.6328193
## NRA3.1 1.5930246
## NRA4.1 -1.9850128
## NRA5.1 .
## NRA6.1 0.3766602
## NRC1.1 3.0487552
## NRC2.1 1.0553288
## NRC3.1 0.3071700
## NRC4.1 .
## NRC5.1 -4.1022191
## NRC6.1 .
## sex .
## age -0.8293940
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5604326665
## NRN1.1 4.2693801686
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.2323565515
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.6451853675
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.4813887161
## NRO5.1 0.4003091099
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.2104982723
## NRA3.1 1.1715749031
## NRA4.1 1.2095038953
## NRA5.1 0.7968693565
## NRA6.1 -3.0502195056
## NRC1.1 0.0467645473
## NRC2.1 0.6353357449
## NRC3.1 .
## NRC4.1 0.0008856741
## NRC5.1 0.3013465220
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.420269
## NRN1.1 1.542315
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.47519529
## NRN1.1 2.09874070
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.26141318
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.60544727
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.74931707
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.82712953
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 2.52121959
## NRA6.1 -1.58033889
## NRC1.1 1.64080860
## NRC2.1 0.09084807
## NRC3.1 -0.05510963
## NRC4.1 1.04761957
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.837671
## NRN1.1 1.565979
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.03100459
## NRN1.1 2.29262332
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.01624192
## NRE4.1 0.04912902
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.27198067
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.48781652
## NRA3.1 .
## NRA4.1 0.59131165
## NRA5.1 1.34011002
## NRA6.1 -0.56649978
## NRC1.1 0.59421982
## NRC2.1 0.58161810
## NRC3.1 .
## NRC4.1 0.63124422
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.40700104
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.12743396
## NRN1.1 1.50912159
## NRN2.1 .
## NRN3.1 1.36004989
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 1.95204693
## NRE4.1 .
## NRE5.1 1.31667382
## NRE6.1 -2.28065578
## NRO1.1 .
## NRO2.1 .
## NRO3.1 -0.01755742
## NRO4.1 .
## NRO5.1 -0.02714804
## NRO6.1 .
## NRA1.1 -0.57951583
## NRA2.1 0.45579037
## NRA3.1 1.48540750
## NRA4.1 .
## NRA5.1 0.88453401
## NRA6.1 .
## NRC1.1 1.78894872
## NRC2.1 .
## NRC3.1 0.54989963
## NRC4.1 .
## NRC5.1 -1.47761818
## NRC6.1 .
## sex 1.19448872
## age .
## running cv # 7
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.99137588
## NRN1.1 1.48272456
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.30782674
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.42198435
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.44646958
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.36398140
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.59764032
## NRA3.1 .
## NRA4.1 0.04025983
## NRA5.1 2.26567119
## NRA6.1 -1.09391881
## NRC1.1 0.46674408
## NRC2.1 1.18325246
## NRC3.1 .
## NRC4.1 0.12564183
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.9765890
## NRN1.1 1.0936816
## NRN2.1 .
## NRN3.1 0.7510645
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.50381267
## NRN1.1 2.63448905
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.02184594
## NRO1.1 0.95564905
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.18079427
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.82500648
## NRA3.1 .
## NRA4.1 1.37430654
## NRA5.1 1.79939752
## NRA6.1 -1.77537078
## NRC1.1 0.12843757
## NRC2.1 0.95246791
## NRC3.1 .
## NRC4.1 0.51239794
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.16869529
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9707642
## NRN1.1 1.6359966
## NRN2.1 .
## NRN3.1 0.1833824
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.1320781
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.85697788
## NRN1.1 2.85042945
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.20957155
## NRE1.1 -1.41296631
## NRE2.1 0.75376728
## NRE3.1 0.66526194
## NRE4.1 1.04723358
## NRE5.1 -0.15171250
## NRE6.1 -0.46633301
## NRO1.1 1.80360825
## NRO2.1 -0.75600656
## NRO3.1 .
## NRO4.1 0.04136283
## NRO5.1 .
## NRO6.1 -0.05716027
## NRA1.1 .
## NRA2.1 0.71395594
## NRA3.1 0.79817389
## NRA4.1 1.65939182
## NRA5.1 1.15223029
## NRA6.1 -0.94244679
## NRC1.1 0.52295710
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.56328625
## NRC5.1 .
## NRC6.1 .
## sex -0.52292069
## age 0.61898631
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5522048
## NRN1.1 0.5136309
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -1.0465687
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age -0.5545973
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.6952150
## NRN1.1 1.9158673
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.3319027
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.6369776
## NRA1.1 .
## NRA2.1 0.8154397
## NRA3.1 .
## NRA4.1 0.8165284
## NRA5.1 1.9332968
## NRA6.1 -0.9098110
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.2809490
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.1463782
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.191535
## NRN1.1 1.481289
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 1.020733
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.54037663
## NRN1.1 1.97212446
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.86066571
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.82134333
## NRA3.1 0.01892007
## NRA4.1 0.43212754
## NRA5.1 0.38479968
## NRA6.1 -0.54018608
## NRC1.1 0.40891913
## NRC2.1 0.37774668
## NRC3.1 .
## NRC4.1 0.13343854
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.18057013
## NRN1.1 0.87812707
## NRN2.1 0.02761007
## NRN3.1 2.59452826
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.92429566
## NRE2.1 .
## NRE3.1 1.99406541
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 -0.07914978
## NRO6.1 .
## NRA1.1 -1.40058103
## NRA2.1 .
## NRA3.1 0.43857208
## NRA4.1 .
## NRA5.1 0.36279545
## NRA6.1 .
## NRC1.1 3.70871221
## NRC2.1 0.39968945
## NRC3.1 0.31926037
## NRC4.1 -0.60484012
## NRC5.1 -2.66671203
## NRC6.1 .
## sex 1.69707478
## age .
## running cv # 8
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.25072230
## NRN1.1 1.98039208
## NRN2.1 0.35831583
## NRN3.1 -0.49324047
## NRN4.1 0.21792804
## NRN5.1 0.81098504
## NRN6.1 0.05695986
## NRE1.1 -1.77567625
## NRE2.1 0.51909896
## NRE3.1 0.95870473
## NRE4.1 -0.35158739
## NRE5.1 0.78484713
## NRE6.1 -1.14288126
## NRO1.1 2.31034342
## NRO2.1 -1.05060695
## NRO3.1 0.14642604
## NRO4.1 0.40445044
## NRO5.1 0.61043938
## NRO6.1 -0.73640555
## NRA1.1 -0.59362466
## NRA2.1 0.33846482
## NRA3.1 1.56435502
## NRA4.1 1.77359403
## NRA5.1 2.14216072
## NRA6.1 -0.44800522
## NRC1.1 0.36537164
## NRC2.1 0.96502103
## NRC3.1 -0.48720305
## NRC4.1 1.22874621
## NRC5.1 -0.67078157
## NRC6.1 -0.67119243
## sex -1.56181427
## age 2.00839679
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8150125
## NRN1.1 0.5405704
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age -0.0221390
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.877337251
## NRN1.1 3.124532464
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.030845627
## NRE2.1 .
## NRE3.1 0.323040164
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.914873656
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.006198148
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.879238918
## NRA3.1 0.105701695
## NRA4.1 0.553181149
## NRA5.1 0.974390740
## NRA6.1 -1.560585867
## NRC1.1 0.021889438
## NRC2.1 0.434102650
## NRC3.1 .
## NRC4.1 0.632624095
## NRC5.1 0.124715358
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.25398613
## NRN1.1 0.46384129
## NRN2.1 .
## NRN3.1 0.13513358
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.18181017
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -0.08889736
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.59949951
## NRN1.1 2.50854788
## NRN2.1 0.01295659
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.21055822
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.03873843
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.30965000
## NRO5.1 .
## NRO6.1 0.07235729
## NRA1.1 .
## NRA2.1 0.63198860
## NRA3.1 .
## NRA4.1 1.16985875
## NRA5.1 1.95510836
## NRA6.1 -1.17829544
## NRC1.1 0.55893323
## NRC2.1 0.61922087
## NRC3.1 .
## NRC4.1 0.54602562
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.98647875
## NRN1.1 1.91809918
## NRN2.1 0.48759767
## NRN3.1 1.44838472
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.09838163
## NRE2.1 .
## NRE3.1 1.41432987
## NRE4.1 -0.43059369
## NRE5.1 0.05025233
## NRE6.1 .
## NRO1.1 0.28668692
## NRO2.1 0.08592019
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.70870165
## NRC2.1 0.63704367
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -1.70313416
## NRC6.1 .
## sex .
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1804634
## NRN1.1 2.0304101
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.0799989
## NRA3.1 .
## NRA4.1 .
## NRA5.1 1.9794968
## NRA6.1 -1.3275443
## NRC1.1 1.1311383
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.1502696
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.6619669
## NRN1.1 1.3540126
## NRN2.1 .
## NRN3.1 0.6807192
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.05548438
## NRN1.1 1.47948116
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.05274783
## NRE3.1 .
## NRE4.1 0.52318243
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.70149939
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.84020799
## NRA3.1 .
## NRA4.1 1.16365161
## NRA5.1 1.36599481
## NRA6.1 -0.87383959
## NRC1.1 .
## NRC2.1 0.03286067
## NRC3.1 .
## NRC4.1 0.39968327
## NRC5.1 0.50729456
## NRC6.1 .
## sex .
## age 0.49947948
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.58615324
## NRN1.1 1.86735166
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 0.02071626
## NRC4.1 .
## NRC5.1 -0.29508059
## NRC6.1 .
## sex .
## age .
## running cv # 9
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.23097808
## NRN1.1 1.79092075
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.78690804
## NRO2.1 -0.53916922
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.25822428
## NRA3.1 .
## NRA4.1 0.06132541
## NRA5.1 2.49382008
## NRA6.1 -0.27281365
## NRC1.1 1.87188667
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.47938516
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.8087467
## NRN1.1 2.3716887
## NRN2.1 0.9684488
## NRN3.1 1.6413985
## NRN4.1 .
## NRN5.1 -1.6206860
## NRN6.1 .
## NRE1.1 -3.1538795
## NRE2.1 .
## NRE3.1 2.1854352
## NRE4.1 .
## NRE5.1 .
## NRE6.1 1.0856219
## NRO1.1 .
## NRO2.1 -0.8188719
## NRO3.1 1.0470266
## NRO4.1 -0.0738544
## NRO5.1 -1.0320676
## NRO6.1 .
## NRA1.1 .
## NRA2.1 -0.7488550
## NRA3.1 1.3067842
## NRA4.1 -1.1416032
## NRA5.1 .
## NRA6.1 0.8806127
## NRC1.1 3.9607252
## NRC2.1 3.3026316
## NRC3.1 1.7017732
## NRC4.1 1.0788071
## NRC5.1 -6.4863082
## NRC6.1 -1.5258338
## sex 1.0993449
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5508230
## NRN1.1 2.3713835
## NRN2.1 1.0891554
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.5864835
## NRE4.1 0.2471826
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.0337221
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.1125436
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.1084242
## NRA3.1 0.1969869
## NRA4.1 0.9800731
## NRA5.1 1.5634549
## NRA6.1 -1.8993866
## NRC1.1 0.3634571
## NRC2.1 0.5124028
## NRC3.1 .
## NRC4.1 0.2767789
## NRC5.1 0.3001565
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4897053
## NRN1.1 0.8168315
## NRN2.1 .
## NRN3.1 0.9886613
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.4034886
## NRN1.1 1.7123509
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.9997193
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.3160798
## NRA3.1 .
## NRA4.1 0.9881541
## NRA5.1 2.5976946
## NRA6.1 -0.1811872
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.737293
## NRN1.1 1.084062
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.7952114
## NRN1.1 3.1900949
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.4621083
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.2457721
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.3775292
## NRA3.1 .
## NRA4.1 1.6646047
## NRA5.1 0.6162651
## NRA6.1 -1.5787112
## NRC1.1 .
## NRC2.1 0.5539023
## NRC3.1 .
## NRC4.1 0.2422559
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.4552490
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.32358161
## NRN1.1 0.87497028
## NRN2.1 .
## NRN3.1 0.63416393
## NRN4.1 .
## NRN5.1 -1.07202976
## NRN6.1 .
## NRE1.1 -2.76930211
## NRE2.1 .
## NRE3.1 2.05037368
## NRE4.1 -2.40562541
## NRE5.1 1.24833628
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.05765023
## NRO5.1 .
## NRO6.1 -0.82247465
## NRA1.1 -0.48194812
## NRA2.1 1.58384140
## NRA3.1 2.55012494
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.60701326
## NRC2.1 1.07363119
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -2.69785662
## NRC6.1 .
## sex 1.78018915
## age -0.87422779
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.9158051
## NRN1.1 1.9701054
## NRN2.1 0.2093638
## NRN3.1 -0.5015855
## NRN4.1 0.2315255
## NRN5.1 0.7497310
## NRN6.1 1.4498244
## NRE1.1 -1.5090847
## NRE2.1 0.4750413
## NRE3.1 0.8030802
## NRE4.1 0.3143999
## NRE5.1 1.2620628
## NRE6.1 -2.0354419
## NRO1.1 2.4269861
## NRO2.1 -1.2704745
## NRO3.1 -0.1097335
## NRO4.1 -0.2339959
## NRO5.1 -0.3098283
## NRO6.1 0.1500600
## NRA1.1 -0.4927922
## NRA2.1 0.5607075
## NRA3.1 0.9162075
## NRA4.1 1.4713263
## NRA5.1 1.6320250
## NRA6.1 -1.6880180
## NRC1.1 2.1013826
## NRC2.1 1.1345203
## NRC3.1 -1.8748074
## NRC4.1 1.5695632
## NRC5.1 -1.1014805
## NRC6.1 -0.2675307
## sex 0.9924779
## age 1.2720435
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.43715
## NRN1.1 1.30123
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running cv # 10
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1428533
## NRN1.1 2.3302532
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.1765313
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2822496
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7183336
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.1044504
## NRO5.1 .
## NRO6.1 0.2655910
## NRA1.1 .
## NRA2.1 0.7430308
## NRA3.1 .
## NRA4.1 0.5855615
## NRA5.1 1.8663578
## NRA6.1 -1.2060299
## NRC1.1 0.2752044
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.5992411
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.3573203
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.26611
## NRN1.1 1.91497
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.530652253
## NRN1.1 2.729433701
## NRN2.1 0.172905252
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.008667743
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.018769393
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.195349548
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.518070147
## NRA3.1 .
## NRA4.1 0.957623625
## NRA5.1 1.015517252
## NRA6.1 -0.917503708
## NRC1.1 .
## NRC2.1 0.930886303
## NRC3.1 .
## NRC4.1 0.543723825
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.31103167
## NRN1.1 0.55175610
## NRN2.1 .
## NRN3.1 0.10941773
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.05057179
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.63736153
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.08014789
## NRN1.1 2.14047153
## NRN2.1 0.22762093
## NRN3.1 -0.59756724
## NRN4.1 0.20543380
## NRN5.1 0.94294633
## NRN6.1 -0.74637394
## NRE1.1 -1.53859335
## NRE2.1 1.40513809
## NRE3.1 0.92568411
## NRE4.1 -0.56855914
## NRE5.1 0.47635855
## NRE6.1 -1.14277087
## NRO1.1 1.25072938
## NRO2.1 -1.42998387
## NRO3.1 0.12070284
## NRO4.1 1.20029728
## NRO5.1 1.24511085
## NRO6.1 -0.96606212
## NRA1.1 -0.23282884
## NRA2.1 1.35226620
## NRA3.1 1.35837612
## NRA4.1 0.01385867
## NRA5.1 2.89657526
## NRA6.1 -1.18313136
## NRC1.1 0.79606562
## NRC2.1 1.16173891
## NRC3.1 -0.94454275
## NRC4.1 0.46867428
## NRC5.1 0.03901415
## NRC6.1 -0.18299322
## sex -1.03407825
## age 0.32489983
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.93270119
## NRN1.1 0.86402175
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.03147322
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.7853428
## NRN1.1 2.3451789
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.1557212
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.5088565
## NRA3.1 .
## NRA4.1 1.2962635
## NRA5.1 1.6555020
## NRA6.1 -1.4590544
## NRC1.1 0.7259853
## NRC2.1 0.1825067
## NRC3.1 .
## NRC4.1 0.3733745
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.47326795
## NRN1.1 1.80179779
## NRN2.1 .
## NRN3.1 0.42809122
## NRN4.1 .
## NRN5.1 -0.34216118
## NRN6.1 .
## NRE1.1 -1.06099476
## NRE2.1 .
## NRE3.1 1.39253080
## NRE4.1 -0.17835029
## NRE5.1 .
## NRE6.1 -0.63093159
## NRO1.1 .
## NRO2.1 -0.01919615
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.83607793
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.03153172
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -1.08321831
## NRC6.1 .
## sex .
## age -0.98483579
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5964295
## NRN1.1 2.6617237
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.1227592
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.2496727
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.8307526
## NRA3.1 .
## NRA4.1 1.8016086
## NRA5.1 1.1932650
## NRA6.1 -1.0263557
## NRC1.1 1.3391898
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.9246452
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.55724747
## NRN1.1 0.57036360
## NRN2.1 .
## NRN3.1 1.08652976
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.03005363
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .mult.pred.m3.bdi = as.data.frame(mult.pred.m3.bdi)
mult.pred.m3.bdi$mean = rowMeans(cbind(mult.pred.m3.bdi$V1, mult.pred.m3.bdi$V2, mult.pred.m3.bdi$V3, mult.pred.m3.bdi$V4, mult.pred.m3.bdi$V5,
mult.pred.m3.bdi$V6, mult.pred.m3.bdi$V7, mult.pred.m3.bdi$V8, mult.pred.m3.bdi$V9, mult.pred.m3.bdi$V10))
describe(abs(mult.pred.m3.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 3.37 2.51 2.81 3.13 2.46 0.05 13.91 13.86 0.99 0.99
## se Q0.25 Q0.75
## 1 0.17 1.38 5.04mult.pred.m3.bdi = cbind(mult.pred.m3.bdi, bdi.pai)
mult.pred.m3.bdi$indication = if_else(mult.pred.m3.bdi$mean < 0, 0, 1)
cbt.indicated.m3.bdi = filter(mult.pred.m3.bdi, indication == 0)
med.indicated.m3.bdi = filter(mult.pred.m3.bdi, indication == 1)
# Analyses across tx group
mult.pred.m3.bdi$match = if_else(mult.pred.m3.bdi$indication==mult.pred.m3.bdi$txgrp, 0, 1)
describeBy(mult.pred.m3.bdi$y, group = mult.pred.m3.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 100 15 10.87 14 13.98 10.38 0 47 47 0.79 0.31
## se Q0.25 Q0.75
## 1 1.09 7 21
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 108 17.36 11.57 14.5 16.54 12.6 0 51 51 0.63 -0.41
## se Q0.25 Q0.75
## 1 1.11 8.75 26t.test(mult.pred.m3.bdi$y ~ mult.pred.m3.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m3.bdi$y by mult.pred.m3.bdi$match
## t = -1.5186, df = 205.96, p-value = 0.1304
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.4296534 0.7046909
## sample estimates:
## mean in group 0 mean in group 1
## 14.99705 17.35953cohen.d(mult.pred.m3.bdi$y ~ mult.pred.m3.bdi$match)##
## Cohen's d
##
## d estimate: -0.2102368 (small)
## 95 percent confidence interval:
## lower upper
## -0.48459725 0.06412366#### Model 4 (Pre-tx BDI-II and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(bdi.pai) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(bdi.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(bdi.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = bdi.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:31, 34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:31)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = bdi.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:31)]
X_train_med_data = bdi.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:31, 34)]
X_train_med_enfunc = X_train_med_data[,c(1:31)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = bdi.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:31)]
bdi.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
bdi.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(bdi.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(bdi.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(bdi.cbt.model4, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(bdi.med.model4, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m4.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9252084
## NRN1.1 0.7728312
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.8517168
## NRE2.1 .
## NRE3.1 0.8003363
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7825358
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.4875438
## NRA5.1 1.0304691
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.1301143
## NRC3.1 .
## NRC4.1 0.6631008
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.3602389
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8127010
## NRN1.1 0.7886895
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.5798641
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3925050
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.399601351
## NRN1.1 2.143823690
## NRN2.1 1.110682893
## NRN3.1 -1.550739648
## NRN4.1 -0.006205294
## NRN5.1 0.634928797
## NRN6.1 -0.509215565
## NRE1.1 -1.737707961
## NRE2.1 1.656037140
## NRE3.1 0.785517922
## NRE4.1 0.090344675
## NRE5.1 0.342269456
## NRE6.1 -1.434470239
## NRO1.1 1.002645975
## NRO2.1 -1.062814404
## NRO3.1 -0.020528004
## NRO4.1 1.069736704
## NRO5.1 0.749117647
## NRO6.1 -1.473102290
## NRA1.1 -0.052897222
## NRA2.1 0.920365565
## NRA3.1 1.766848228
## NRA4.1 1.905367704
## NRA5.1 2.311644314
## NRA6.1 -1.893774567
## NRC1.1 0.869114834
## NRC2.1 0.245589669
## NRC3.1 -1.137209160
## NRC4.1 0.533093943
## NRC5.1 -0.297622857
## NRC6.1 0.465894667
## BDI2.1 2.052173751
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.458326
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.453546
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.3998576
## NRN1.1 2.4029818
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5755829
## NRE2.1 .
## NRE3.1 0.2844199
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.4128457
## NRO1.1 1.2652301
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.4792728
## NRA3.1 0.4461633
## NRA4.1 1.3700588
## NRA5.1 0.9834656
## NRA6.1 -1.1906080
## NRC1.1 0.4301867
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.9463401
## NRC5.1 0.2447761
## NRC6.1 .
## BDI2.1 1.1965022
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.08194115
## NRN1.1 0.34612072
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.05567126
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.09068746
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.30647236
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.32859389
## NRN1.1 1.93015736
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.20593753
## NRN6.1 0.02702439
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.26102940
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.70367663
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.27640566
## NRA1.1 .
## NRA2.1 0.76070714
## NRA3.1 .
## NRA4.1 1.37836493
## NRA5.1 1.95048987
## NRA6.1 -0.75925820
## NRC1.1 0.65324943
## NRC2.1 0.24370052
## NRC3.1 .
## NRC4.1 0.07709064
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.06738063
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.430454
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.975752
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.82931615
## NRN1.1 1.85161650
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.73103875
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.02441370
## NRO5.1 .
## NRO6.1 0.11289229
## NRA1.1 .
## NRA2.1 0.30012626
## NRA3.1 .
## NRA4.1 1.74419362
## NRA5.1 1.48301133
## NRA6.1 -1.27681130
## NRC1.1 0.08583039
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.62559367
## NRC5.1 0.30486112
## NRC6.1 0.35263855
## BDI2.1 1.26630264
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8058335
## NRN1.1 0.5750457
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.1314921
## running cv # 2
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.31400830
## NRN1.1 1.70080385
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.50814595
## NRE2.1 0.05260394
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.98111139
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.01298358
## NRA3.1 .
## NRA4.1 1.16015730
## NRA5.1 0.88089902
## NRA6.1 -0.47984176
## NRC1.1 0.63169540
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.95256121
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.00567856
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4609839
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.6483598
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.1561389
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.12025754
## NRN1.1 0.80976672
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.02569238
## NRE3.1 0.53960933
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.54884044
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.25161862
## NRA1.1 .
## NRA2.1 0.20674986
## NRA3.1 .
## NRA4.1 0.85877443
## NRA5.1 1.66752180
## NRA6.1 -0.40963016
## NRC1.1 .
## NRC2.1 0.97189941
## NRC3.1 .
## NRC4.1 0.58099673
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.38811904
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7136850
## NRN1.1 0.5788151
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.2786835
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 -0.1850729
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.3880308
## NRC2.1 .
## NRC3.1 0.5563874
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.4500069
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.1856897
## NRN1.1 1.3644133
## NRN2.1 .
## NRN3.1 -0.1472360
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.1761095
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.1821136
## NRE6.1 -0.2118483
## NRO1.1 1.6835816
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.1801762
## NRO5.1 0.5090111
## NRO6.1 0.2194876
## NRA1.1 .
## NRA2.1 0.4813387
## NRA3.1 .
## NRA4.1 1.4702723
## NRA5.1 2.4163902
## NRA6.1 -1.1291770
## NRC1.1 1.4935675
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.6337005
## NRC5.1 0.1433736
## NRC6.1 0.2375093
## BDI2.1 1.4250997
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8575700
## NRN1.1 0.5468227
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.3380296
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1178736
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3029022
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.56524886
## NRN1.1 2.93209828
## NRN2.1 0.05525516
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.03447889
## NRE1.1 -0.70262414
## NRE2.1 0.10446844
## NRE3.1 0.94622254
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.06025851
## NRO1.1 0.99442425
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.15700580
## NRO5.1 0.41532568
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.79719552
## NRA3.1 0.99866913
## NRA4.1 1.78401743
## NRA5.1 1.03103197
## NRA6.1 -1.82240805
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.40443552
## NRC5.1 1.03005820
## NRC6.1 .
## BDI2.1 1.44353868
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.5746523
## NRN1.1 0.6922897
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.7620247
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.0483211
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.17142070
## NRN1.1 1.85493272
## NRN2.1 0.38234001
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.27259547
## NRO2.1 -0.04727096
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 0.08411143
## NRA2.1 .
## NRA3.1 .
## NRA4.1 1.84022434
## NRA5.1 1.95016914
## NRA6.1 -1.90360264
## NRC1.1 0.16189710
## NRC2.1 1.08866840
## NRC3.1 .
## NRC4.1 1.14580014
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.54641051
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9080174
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.3183433
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.7880025
## running cv # 3
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1275550
## NRN1.1 1.7637624
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.6789789
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.8687566
## NRA3.1 .
## NRA4.1 0.9457122
## NRA5.1 1.4304605
## NRA6.1 -1.0343477
## NRC1.1 .
## NRC2.1 1.2657996
## NRC3.1 .
## NRC4.1 0.4631723
## NRC5.1 0.1327265
## NRC6.1 .
## BDI2.1 1.2892382
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.704380
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.615254
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.92732163
## NRN1.1 3.73800556
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.84988715
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.76096059
## NRE6.1 -0.07399995
## NRO1.1 1.58693967
## NRO2.1 .
## NRO3.1 0.29954163
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.10746590
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.50328067
## NRA4.1 1.49207278
## NRA5.1 2.65046258
## NRA6.1 -2.46722212
## NRC1.1 0.94033396
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.02223430
## NRC5.1 0.29788911
## NRC6.1 .
## BDI2.1 1.23539182
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.1627001
## NRN1.1 0.5631020
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.9164020
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.2533836
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.5336509
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.81629512
## NRN1.1 0.47745809
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.44975091
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.09840531
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.14702878
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.63119312
## NRO5.1 .
## NRO6.1 0.13371563
## NRA1.1 .
## NRA2.1 0.33833920
## NRA3.1 .
## NRA4.1 0.21042822
## NRA5.1 1.93321630
## NRA6.1 -1.09271396
## NRC1.1 0.69959525
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.80996536
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.55238966
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.021941
## NRN1.1 0.464029
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.402929
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.564177
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1890958
## NRN1.1 0.9292813
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 1.0290353
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.5624417
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.2543014
## NRA3.1 .
## NRA4.1 1.7495182
## NRA5.1 0.6676692
## NRA6.1 -0.6807367
## NRC1.1 .
## NRC2.1 0.2097247
## NRC3.1 .
## NRC4.1 0.7199252
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.4633900
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 18.42781910
## NRN1.1 1.03370618
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.13657574
## NRN5.1 -0.52407737
## NRN6.1 .
## NRE1.1 -1.46352236
## NRE2.1 .
## NRE3.1 2.68846782
## NRE4.1 -1.10435542
## NRE5.1 0.18187064
## NRE6.1 -0.69201229
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.36504478
## NRA1.1 -0.36030522
## NRA2.1 -0.01510165
## NRA3.1 .
## NRA4.1 -0.34233542
## NRA5.1 .
## NRA6.1 0.12085790
## NRC1.1 2.13013938
## NRC2.1 .
## NRC3.1 1.92886126
## NRC4.1 .
## NRC5.1 -1.62856497
## NRC6.1 0.36617123
## BDI2.1 5.21918641
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5163730
## NRN1.1 1.6532179
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.6155848
## NRE2.1 .
## NRE3.1 0.3195260
## NRE4.1 0.1730716
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7325896
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.0100169
## NRA3.1 .
## NRA4.1 1.9533752
## NRA5.1 0.6392263
## NRA6.1 -0.2683307
## NRC1.1 0.5002081
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.4779413
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.4281225
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4405797
## NRN1.1 1.4061209
## NRN2.1 0.3138970
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.8118764
## NRE2.1 .
## NRE3.1 0.4252695
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 1.1733467
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.6513353
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.5987973
## NRC2.1 1.6014966
## NRC3.1 0.8307019
## NRC4.1 .
## NRC5.1 -2.7212206
## NRC6.1 .
## BDI2.1 4.0544537
## running cv # 4
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.0294390
## NRN1.1 1.7200925
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.5936424
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7390578
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.8718479
## NRA3.1 0.1540660
## NRA4.1 1.9311365
## NRA5.1 1.0326333
## NRA6.1 -0.8192884
## NRC1.1 .
## NRC2.1 0.5181549
## NRC3.1 .
## NRC4.1 0.6383382
## NRC5.1 0.5111673
## NRC6.1 .
## BDI2.1 1.3996711
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.48838857
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.08957942
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.38827477
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.04471876
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.58169669
## NRN1.1 2.83021838
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.62859997
## NRE4.1 0.05461736
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.13188722
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.55259913
## NRA3.1 .
## NRA4.1 0.56939676
## NRA5.1 0.59792318
## NRA6.1 -1.50772832
## NRC1.1 .
## NRC2.1 0.21501604
## NRC3.1 .
## NRC4.1 0.18842402
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.77010717
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.12831473
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.59978400
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.02360053
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 0.20290965
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.10037147
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.8957883
## NRN1.1 1.2217542
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7462474
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.4567997
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.7154271
## NRA3.1 .
## NRA4.1 1.3391047
## NRA5.1 1.7834154
## NRA6.1 -0.9025652
## NRC1.1 0.5801179
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.1414245
## NRC5.1 0.4041560
## NRC6.1 .
## BDI2.1 1.5642565
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.11213501
## NRN1.1 0.03531658
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.49500321
## BDI2.1 2.80396466
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.5659900
## NRN1.1 2.2037209
## NRN2.1 .
## NRN3.1 -0.1134399
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.1105066
## NRE1.1 -0.5108186
## NRE2.1 0.5419518
## NRE3.1 0.1225911
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.7792211
## NRO1.1 1.4494094
## NRO2.1 -0.5139953
## NRO3.1 .
## NRO4.1 0.3694684
## NRO5.1 0.1864584
## NRO6.1 .
## NRA1.1 0.1242331
## NRA2.1 0.4011345
## NRA3.1 0.6068303
## NRA4.1 1.6147856
## NRA5.1 1.3883133
## NRA6.1 -1.4934348
## NRC1.1 0.4171943
## NRC2.1 0.5027611
## NRC3.1 .
## NRC4.1 0.7076673
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.1292192
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4162444
## NRN1.1 0.3821643
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.3004042
## NRC2.1 .
## NRC3.1 0.1299437
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.8893620
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.30825508
## NRN1.1 0.99166442
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.32677666
## NRN6.1 .
## NRE1.1 -0.66936045
## NRE2.1 0.40276377
## NRE3.1 0.27655278
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.68395128
## NRO2.1 -0.02807941
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.79582125
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.74567032
## NRA5.1 2.09204077
## NRA6.1 -0.84988086
## NRC1.1 1.18163385
## NRC2.1 1.15383364
## NRC3.1 -0.64966688
## NRC4.1 1.31471418
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.20434061
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3054741
## NRN1.1 0.4810861
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.0310117
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3350732
## running cv # 5
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.254011612
## NRN1.1 3.280101494
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.570047287
## NRE2.1 .
## NRE3.1 0.284054460
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.537357947
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.273167008
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.708041275
## NRA3.1 0.007600466
## NRA4.1 0.863166764
## NRA5.1 0.907704876
## NRA6.1 -1.590173429
## NRC1.1 0.462422644
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.773947291
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.683668478
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.8600184
## NRN1.1 0.6239946
## NRN2.1 0.1347649
## NRN3.1 .
## NRN4.1 0.6430570
## NRN5.1 -0.1654317
## NRN6.1 .
## NRE1.1 -0.6039764
## NRE2.1 .
## NRE3.1 1.4987551
## NRE4.1 -0.3855713
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.6951918
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.1581833
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.1730121
## NRC2.1 0.8564266
## NRC3.1 1.7594855
## NRC4.1 -0.2605250
## NRC5.1 -1.7238326
## NRC6.1 .
## BDI2.1 3.0525215
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.06105004
## NRN1.1 1.85269920
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.01448377
## NRE3.1 0.06459973
## NRE4.1 0.26835007
## NRE5.1 0.89341054
## NRE6.1 .
## NRO1.1 0.93993256
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 -0.05639096
## NRO6.1 0.03403343
## NRA1.1 -0.11627550
## NRA2.1 0.92933864
## NRA3.1 0.35131606
## NRA4.1 2.01097093
## NRA5.1 1.23953562
## NRA6.1 -0.60587641
## NRC1.1 0.21527634
## NRC2.1 .
## NRC3.1 -0.24772577
## NRC4.1 0.97788188
## NRC5.1 0.59153227
## NRC6.1 0.03479066
## BDI2.1 1.73247868
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4095410
## NRN1.1 1.8667568
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.2432836
## NRN6.1 .
## NRE1.1 -0.9857320
## NRE2.1 .
## NRE3.1 1.1722114
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.3614727
## NRC2.1 .
## NRC3.1 0.7874441
## NRC4.1 .
## NRC5.1 -1.9915172
## NRC6.1 .
## BDI2.1 3.7055336
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.91051640
## NRN1.1 1.56894330
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.44676279
## NRE2.1 0.86332499
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.08487078
## NRE6.1 .
## NRO1.1 0.76786782
## NRO2.1 -0.97628709
## NRO3.1 .
## NRO4.1 0.01689509
## NRO5.1 .
## NRO6.1 .
## NRA1.1 0.49736478
## NRA2.1 0.60774202
## NRA3.1 .
## NRA4.1 0.74119753
## NRA5.1 2.34821743
## NRA6.1 -1.08198815
## NRC1.1 1.56577835
## NRC2.1 .
## NRC3.1 -0.62088710
## NRC4.1 0.96239747
## NRC5.1 .
## NRC6.1 0.23909860
## BDI2.1 2.21108400
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3245631
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1640365
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.2628945
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.10349566
## NRN1.1 0.35436527
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.28603020
## NRA1.1 .
## NRA2.1 0.16168463
## NRA3.1 .
## NRA4.1 0.59515019
## NRA5.1 1.22612261
## NRA6.1 .
## NRC1.1 0.02773582
## NRC2.1 1.73647932
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.69597592
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.98821423
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.46372347
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.03787967
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.35985296
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.25001024
## NRN1.1 1.67063142
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.01340070
## NRE2.1 .
## NRE3.1 0.41475270
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.77116907
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.04111618
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.12223174
## NRA3.1 .
## NRA4.1 1.53955451
## NRA5.1 1.00643959
## NRA6.1 -1.13114248
## NRC1.1 .
## NRC2.1 0.61798666
## NRC3.1 .
## NRC4.1 0.51694675
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.35913128
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8398495
## NRN1.1 0.3914548
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.5078744
## NRC2.1 0.1604107
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.4116525
## running cv # 6
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.95865229
## NRN1.1 1.17914404
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.39479038
## NRE1.1 -0.89537051
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.40264982
## NRO2.1 -0.58031023
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.56808132
## NRA3.1 .
## NRA4.1 1.41652604
## NRA5.1 1.84589388
## NRA6.1 -0.57853903
## NRC1.1 0.08620958
## NRC2.1 0.35742259
## NRC3.1 .
## NRC4.1 1.10971929
## NRC5.1 .
## NRC6.1 0.08356991
## BDI2.1 1.11429334
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.33960165
## NRN1.1 1.07698077
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -1.63041592
## NRN6.1 0.04711918
## NRE1.1 -2.46956595
## NRE2.1 .
## NRE3.1 2.45025680
## NRE4.1 -1.31954074
## NRE5.1 .
## NRE6.1 0.17588860
## NRO1.1 1.20766675
## NRO2.1 -0.01910159
## NRO3.1 0.22354797
## NRO4.1 -0.31280563
## NRO5.1 -0.42626194
## NRO6.1 .
## NRA1.1 -0.43272408
## NRA2.1 0.05851040
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.75744981
## NRA6.1 0.00880212
## NRC1.1 1.56177641
## NRC2.1 0.58840483
## NRC3.1 1.39955057
## NRC4.1 .
## NRC5.1 -2.83156267
## NRC6.1 .
## BDI2.1 4.31955188
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.29884101
## NRN1.1 1.83194279
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.24461182
## NRE2.1 0.06795245
## NRE3.1 0.41122637
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.46959873
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.61479330
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.48483557
## NRA3.1 0.24003965
## NRA4.1 2.00050717
## NRA5.1 1.93310588
## NRA6.1 -1.47660477
## NRC1.1 0.29308542
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.50169934
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.55462230
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8657119
## NRN1.1 0.3859016
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.5957083
## NRN5.1 -0.2930030
## NRN6.1 .
## NRE1.1 -1.8935555
## NRE2.1 .
## NRE3.1 2.2498719
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.1918092
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.5111365
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.3961954
## NRA2.1 0.7571485
## NRA3.1 0.5872736
## NRA4.1 -1.4301913
## NRA5.1 .
## NRA6.1 0.9407797
## NRC1.1 3.2383840
## NRC2.1 1.7737639
## NRC3.1 0.5759430
## NRC4.1 .
## NRC5.1 -3.9190838
## NRC6.1 .
## BDI2.1 3.8975038
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5727973
## NRN1.1 3.2074321
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.2530595
## NRE2.1 .
## NRE3.1 0.2306266
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.4966986
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.2240610
## NRO5.1 0.2648597
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.0180256
## NRA3.1 0.9752145
## NRA4.1 1.6123779
## NRA5.1 0.8083870
## NRA6.1 -2.4485739
## NRC1.1 .
## NRC2.1 0.4652845
## NRC3.1 .
## NRC4.1 0.2330307
## NRC5.1 0.6418226
## NRC6.1 .
## BDI2.1 1.2410914
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.43975026
## NRN1.1 0.65691937
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.03165842
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.63497017
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.5038420
## NRN1.1 1.3366828
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.1372091
## NRE2.1 .
## NRE3.1 0.7309196
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.5038095
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.5944404
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.6108618
## NRA5.1 2.2166752
## NRA6.1 -1.3664016
## NRC1.1 1.6826544
## NRC2.1 0.3207598
## NRC3.1 -0.7321858
## NRC4.1 1.6690021
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.2827767
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.87572522
## NRN1.1 1.29566050
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.18538935
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.46447030
## NRC2.1 .
## NRC3.1 0.03677182
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.09571194
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.926869802
## NRN1.1 1.610044165
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.009296573
## NRE4.1 0.345263144
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.058568336
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.374396415
## NRA3.1 .
## NRA4.1 1.092774425
## NRA5.1 1.128924993
## NRA6.1 -0.395660908
## NRC1.1 0.450509443
## NRC2.1 0.497080849
## NRC3.1 .
## NRC4.1 0.799759966
## NRC5.1 0.257202889
## NRC6.1 .
## BDI2.1 1.656010997
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.04700447
## NRN1.1 0.04516116
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.30529797
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.18883483
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.77988288
## NRC2.1 .
## NRC3.1 0.32002961
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 6.12450470
## running cv # 7
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.23804683
## NRN1.1 0.74721703
## NRN2.1 .
## NRN3.1 0.14121922
## NRN4.1 .
## NRN5.1 0.02597085
## NRN6.1 .
## NRE1.1 -0.83346600
## NRE2.1 1.05920306
## NRE3.1 0.33002638
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.24668627
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.55629351
## NRO5.1 .
## NRO6.1 0.26259037
## NRA1.1 .
## NRA2.1 0.44128985
## NRA3.1 .
## NRA4.1 0.66562286
## NRA5.1 1.97765078
## NRA6.1 -0.98314937
## NRC1.1 0.84832904
## NRC2.1 0.74854590
## NRC3.1 .
## NRC4.1 0.58585668
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.84509932
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.96984726
## NRN1.1 0.52233956
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.04976279
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.80286023
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.37454112
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5257718
## NRN1.1 1.7649676
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8564828
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.1443971
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.5406769
## NRA3.1 .
## NRA4.1 1.8237297
## NRA5.1 1.8536568
## NRA6.1 -1.5372723
## NRC1.1 .
## NRC2.1 1.0208127
## NRC3.1 .
## NRC4.1 1.1254087
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.0052060
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7346162
## NRN1.1 1.0403271
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2737689
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.6919527
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.9691069
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.92655571
## NRN1.1 2.49480464
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.07430962
## NRE1.1 -1.55088099
## NRE2.1 0.84328179
## NRE3.1 0.66079771
## NRE4.1 1.24537973
## NRE5.1 -0.21991910
## NRE6.1 -0.36983189
## NRO1.1 1.58507958
## NRO2.1 -0.76221473
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.07440372
## NRA1.1 .
## NRA2.1 0.70740363
## NRA3.1 0.79749526
## NRA4.1 1.98071178
## NRA5.1 1.11520725
## NRA6.1 -0.91586752
## NRC1.1 0.62056225
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.54747103
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.99869220
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.61290044
## NRN1.1 0.05298443
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -1.16819847
## NRN6.1 0.14173285
## NRE1.1 -2.55346444
## NRE2.1 .
## NRE3.1 1.85818185
## NRE4.1 -1.47670027
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.47812919
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.28445249
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.56164491
## NRA2.1 0.01473570
## NRA3.1 .
## NRA4.1 0.02882040
## NRA5.1 .
## NRA6.1 0.30861171
## NRC1.1 1.88689589
## NRC2.1 1.21900694
## NRC3.1 1.08952963
## NRC4.1 .
## NRC5.1 -2.26886588
## NRC6.1 .
## BDI2.1 4.16246595
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.73152990
## NRN1.1 1.66331266
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.11464672
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.00660805
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.72485693
## NRA1.1 .
## NRA2.1 0.76886143
## NRA3.1 .
## NRA4.1 0.97289592
## NRA5.1 1.82957028
## NRA6.1 -0.91467949
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.42663520
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.82973107
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.26689946
## NRN1.1 1.04778082
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.02426164
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 1.57426449
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 -0.08669631
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.54986865
## NRC2.1 .
## NRC3.1 0.39270571
## NRC4.1 .
## NRC5.1 -0.06270880
## NRC6.1 0.10541780
## BDI2.1 3.96366942
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.6045473
## NRN1.1 1.4720196
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8124948
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.6650343
## NRA3.1 .
## NRA4.1 0.7814488
## NRA5.1 0.1014447
## NRA6.1 -0.3809257
## NRC1.1 0.3140409
## NRC2.1 0.2249548
## NRC3.1 .
## NRC4.1 0.3644647
## NRC5.1 0.1802277
## NRC6.1 .
## BDI2.1 1.4532390
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.91066774
## NRN1.1 0.24085906
## NRN2.1 .
## NRN3.1 0.43439228
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.50571895
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.07751988
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.13578764
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -0.96340005
## NRC6.1 0.37811915
## BDI2.1 3.59382187
## running cv # 8
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.713710292
## NRN1.1 1.095366165
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.425097397
## NRN6.1 .
## NRE1.1 -1.308660310
## NRE2.1 0.213132229
## NRE3.1 0.750396243
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.431841498
## NRO2.1 -0.325140336
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.003772529
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.421748058
## NRA3.1 0.572659060
## NRA4.1 1.739318070
## NRA5.1 1.082260900
## NRA6.1 -0.247974406
## NRC1.1 .
## NRC2.1 0.505497427
## NRC3.1 .
## NRC4.1 0.977691798
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.749791979
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.76749816
## NRN1.1 0.34084798
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.44637142
## NRN6.1 .
## NRE1.1 -0.68035693
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.42122619
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.08217304
## BDI2.1 4.15374916
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.7953245
## NRN1.1 2.5252262
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2968158
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7086829
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.1193792
## NRA1.1 .
## NRA2.1 0.4762251
## NRA3.1 0.1926998
## NRA4.1 1.0173502
## NRA5.1 0.6368318
## NRA6.1 -1.3679452
## NRC1.1 .
## NRC2.1 0.2632434
## NRC3.1 .
## NRC4.1 0.7330328
## NRC5.1 0.4770080
## NRC6.1 .
## BDI2.1 1.4713337
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.1537999
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.5306049
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.7899535
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5341989
## NRN1.1 1.6345465
## NRN2.1 0.3653548
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1522388
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.0643965
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.3567907
## NRO5.1 .
## NRO6.1 0.1100625
## NRA1.1 .
## NRA2.1 0.3042537
## NRA3.1 .
## NRA4.1 1.9187528
## NRA5.1 1.8696053
## NRA6.1 -1.0262552
## NRC1.1 0.6882482
## NRC2.1 0.3462655
## NRC3.1 .
## NRC4.1 0.9473774
## NRC5.1 .
## NRC6.1 0.2539728
## BDI2.1 2.0800133
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.35431552
## NRN1.1 1.53858860
## NRN2.1 0.62381343
## NRN3.1 0.16463361
## NRN4.1 0.03897957
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.39452211
## NRE2.1 .
## NRE3.1 1.85334605
## NRE4.1 -1.24407104
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.52646733
## NRO2.1 0.42089367
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.71814389
## NRC2.1 0.81468911
## NRC3.1 0.83164008
## NRC4.1 .
## NRC5.1 -1.43156753
## NRC6.1 .
## BDI2.1 3.85022864
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.40740651
## NRN1.1 1.99319292
## NRN2.1 .
## NRN3.1 -0.20212169
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.65286560
## NRE2.1 0.03534287
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.70193418
## NRE6.1 -0.31172722
## NRO1.1 0.37550668
## NRO2.1 -0.07620255
## NRO3.1 .
## NRO4.1 0.06469974
## NRO5.1 .
## NRO6.1 0.19109214
## NRA1.1 .
## NRA2.1 0.92726632
## NRA3.1 0.44451030
## NRA4.1 0.88224633
## NRA5.1 2.48688936
## NRA6.1 -2.05276340
## NRC1.1 1.74535113
## NRC2.1 .
## NRC3.1 -0.41982183
## NRC4.1 0.94599887
## NRC5.1 0.14833281
## NRC6.1 .
## BDI2.1 2.23857895
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4710780
## NRN1.1 0.2878932
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1513801
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.4116977
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.046751986
## NRN1.1 1.365644875
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.008273112
## NRE3.1 .
## NRE4.1 0.628578237
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.628335128
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.944809382
## NRA3.1 .
## NRA4.1 1.315377383
## NRA5.1 1.326040103
## NRA6.1 -0.799597804
## NRC1.1 .
## NRC2.1 0.047873333
## NRC3.1 .
## NRC4.1 0.437195013
## NRC5.1 0.571923707
## NRC6.1 .
## BDI2.1 0.391536619
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.80617582
## NRN1.1 0.76030475
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 0.03716092
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.98281405
## running cv # 9
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.18764114
## NRN1.1 1.23837355
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.16773962
## NRE2.1 0.21399864
## NRE3.1 0.18672801
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.53514605
## NRO2.1 -0.59350984
## NRO3.1 -0.06103531
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.10474181
## NRA3.1 .
## NRA4.1 0.44686891
## NRA5.1 2.33536648
## NRA6.1 -0.14867941
## NRC1.1 1.66498076
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.90929169
## NRC5.1 .
## NRC6.1 0.14431107
## BDI2.1 1.80097590
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.20994052
## NRN1.1 1.62304725
## NRN2.1 1.13340683
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.53534779
## NRN6.1 .
## NRE1.1 -1.00003753
## NRE2.1 .
## NRE3.1 1.52189795
## NRE4.1 .
## NRE5.1 .
## NRE6.1 0.09085829
## NRO1.1 .
## NRO2.1 .
## NRO3.1 0.44084389
## NRO4.1 -0.11842478
## NRO5.1 -0.92875914
## NRO6.1 .
## NRA1.1 .
## NRA2.1 -0.11137433
## NRA3.1 .
## NRA4.1 -0.13925071
## NRA5.1 .
## NRA6.1 0.40474849
## NRC1.1 2.72024219
## NRC2.1 1.71875894
## NRC3.1 1.40421956
## NRC4.1 0.04444273
## NRC5.1 -3.31109838
## NRC6.1 .
## BDI2.1 2.75904925
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.58238102
## NRN1.1 1.87896153
## NRN2.1 1.22067772
## NRN3.1 -0.28647561
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.33375448
## NRE2.1 0.19946348
## NRE3.1 0.46163771
## NRE4.1 0.53362447
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.03877505
## NRO2.1 -0.15633062
## NRO3.1 .
## NRO4.1 0.06779221
## NRO5.1 .
## NRO6.1 .
## NRA1.1 0.08087551
## NRA2.1 0.80089533
## NRA3.1 0.44539041
## NRA4.1 1.62344905
## NRA5.1 1.42446944
## NRA6.1 -1.76470750
## NRC1.1 0.48022713
## NRC2.1 0.16997449
## NRC3.1 .
## NRC4.1 0.39092635
## NRC5.1 0.55920231
## NRC6.1 .
## BDI2.1 1.57284743
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4918662
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1890681
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3431325
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.3974961
## NRN1.1 1.8026432
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 1.1460580
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.4401661
## NRA3.1 .
## NRA4.1 1.2550666
## NRA5.1 2.6627678
## NRA6.1 -0.6891909
## NRC1.1 0.2444892
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.6160931
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.5765521
## NRN1.1 1.0675599
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.1764076
## NRN6.1 .
## NRE1.1 -0.8585382
## NRE2.1 .
## NRE3.1 1.4509318
## NRE4.1 -0.7592390
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.4232273
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.0772372
## NRC2.1 .
## NRC3.1 1.2483174
## NRC4.1 .
## NRC5.1 -1.1308721
## NRC6.1 .
## BDI2.1 4.2731550
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.7477251
## NRN1.1 2.2727284
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.3292985
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.2050295
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.9931912
## NRA3.1 .
## NRA4.1 2.3906583
## NRA5.1 0.4491325
## NRA6.1 -1.3494354
## NRC1.1 .
## NRC2.1 0.4772304
## NRC3.1 .
## NRC4.1 0.8315342
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.9078691
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.5281460
## NRN1.1 0.2518550
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -1.7202807
## NRN6.1 .
## NRE1.1 -0.7780530
## NRE2.1 .
## NRE3.1 1.0663021
## NRE4.1 -1.6052324
## NRE5.1 0.7321651
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.3256465
## NRA2.1 1.3048019
## NRA3.1 0.3155619
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.0747245
## NRC2.1 0.7749871
## NRC3.1 0.1826223
## NRC4.1 .
## NRC5.1 -1.6558642
## NRC6.1 0.4359073
## BDI2.1 5.4516469
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5944623
## NRN1.1 1.3982496
## NRN2.1 0.1985605
## NRN3.1 -0.4878153
## NRN4.1 0.2165105
## NRN5.1 0.6784556
## NRN6.1 1.0700021
## NRE1.1 -1.4202760
## NRE2.1 0.4716304
## NRE3.1 0.6110114
## NRE4.1 0.9282499
## NRE5.1 1.0244039
## NRE6.1 -1.3826342
## NRO1.1 1.9938547
## NRO2.1 -0.9328469
## NRO3.1 -0.4283467
## NRO4.1 -0.4509531
## NRO5.1 -0.5138111
## NRO6.1 0.5306500
## NRA1.1 -0.0996898
## NRA2.1 0.6340769
## NRA3.1 0.9258241
## NRA4.1 1.6797468
## NRA5.1 1.8959345
## NRA6.1 -1.7771755
## NRC1.1 2.1989600
## NRC2.1 1.0060833
## NRC3.1 -1.9964655
## NRC4.1 1.5507837
## NRC5.1 -0.6521480
## NRC6.1 0.1623959
## BDI2.1 2.5490607
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.6519246
## NRN1.1 0.2795627
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1665433
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.3280417
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.1033671
## running cv # 10
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.2115236
## NRN1.1 1.7060292
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2884646
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.4526647
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.4607701
## NRA1.1 .
## NRA2.1 0.7241116
## NRA3.1 .
## NRA4.1 0.9682646
## NRA5.1 1.4714474
## NRA6.1 -0.9444303
## NRC1.1 0.0608576
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.8585968
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.6496895
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.315088
## NRN1.1 1.277139
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.947197
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5492861
## NRN1.1 2.0465391
## NRN2.1 0.3593134
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.4064542
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.1341731
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.2441054
## NRA3.1 .
## NRA4.1 1.6091146
## NRA5.1 1.0495674
## NRA6.1 -0.9774344
## NRC1.1 .
## NRC2.1 0.9440506
## NRC3.1 .
## NRC4.1 0.7080462
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.5820000
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.2807882
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.4691928
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.4164503
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.6423045
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.54540883
## NRN1.1 0.71741361
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.19513954
## NRN6.1 .
## NRE1.1 -1.10104180
## NRE2.1 0.60455268
## NRE3.1 0.70858368
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.83557802
## NRO2.1 -0.63514055
## NRO3.1 .
## NRO4.1 0.66979517
## NRO5.1 0.68407499
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.15882801
## NRA3.1 0.79456554
## NRA4.1 .
## NRA5.1 1.85765450
## NRA6.1 -0.70809213
## NRC1.1 0.36913885
## NRC2.1 0.86191436
## NRC3.1 .
## NRC4.1 0.04903115
## NRC5.1 0.04664953
## NRC6.1 .
## BDI2.1 1.94741048
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.65767107
## NRN1.1 0.03852383
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.53445012
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.06858808
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.72259458
## NRN1.1 2.20499981
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.18820633
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.01186426
## NRA1.1 .
## NRA2.1 0.37626111
## NRA3.1 .
## NRA4.1 1.54273178
## NRA5.1 1.66566710
## NRA6.1 -1.54532120
## NRC1.1 0.83853382
## NRC2.1 0.16552252
## NRC3.1 .
## NRC4.1 0.51342231
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.74437933
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3363358
## NRN1.1 0.9946459
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.5162510
## NRN6.1 .
## NRE1.1 -1.4454184
## NRE2.1 .
## NRE3.1 2.0573005
## NRE4.1 -1.2094944
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.1363031
## NRC1.1 0.3332569
## NRC2.1 .
## NRC3.1 0.7705594
## NRC4.1 .
## NRC5.1 -0.4631416
## NRC6.1 .
## BDI2.1 4.1337974
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.58747480
## NRN1.1 2.12787580
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.25408520
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.24816352
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.65481892
## NRA3.1 .
## NRA4.1 2.04452220
## NRA5.1 1.22979828
## NRA6.1 -1.06480009
## NRC1.1 1.40025722
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.23766737
## NRC5.1 0.07795929
## NRC6.1 .
## BDI2.1 1.39248751
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.72074755
## NRN1.1 0.02620021
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.05922291
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.41884693mult.pred.m4.bdi = as.data.frame(mult.pred.m4.bdi)
mult.pred.m4.bdi$mean = rowMeans(cbind(mult.pred.m4.bdi$V1, mult.pred.m4.bdi$V2, mult.pred.m4.bdi$V3, mult.pred.m4.bdi$V4, mult.pred.m4.bdi$V5,
mult.pred.m4.bdi$V6, mult.pred.m4.bdi$V7, mult.pred.m4.bdi$V8, mult.pred.m4.bdi$V9, mult.pred.m4.bdi$V10))
describe(abs(mult.pred.m4.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 3.54 2.59 2.9 3.29 2.59 0.02 12.34 12.32 0.84 0.16
## se Q0.25 Q0.75
## 1 0.18 1.41 5.23mult.pred.m4.bdi = cbind(mult.pred.m4.bdi, bdi.pai)
mult.pred.m4.bdi$indication = if_else(mult.pred.m4.bdi$mean < 0, 0, 1)
cbt.indicated.m4.bdi = filter(mult.pred.m4.bdi, indication == 0)
med.indicated.m4.bdi = filter(mult.pred.m4.bdi, indication == 1)
# Analyses across tx group
mult.pred.m4.bdi$match = if_else(mult.pred.m4.bdi$indication==mult.pred.m4.bdi$txgrp, 0, 1)
describeBy(mult.pred.m4.bdi$y, group = mult.pred.m4.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 97 15.65 10.66 14 14.73 10.38 0 47 47 0.78 0.34
## se Q0.25 Q0.75
## 1 1.08 8 22
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 111 16.73 11.8 14 15.86 11.86 0 51 51 0.64 -0.41
## se Q0.25 Q0.75
## 1 1.12 7 25t.test(mult.pred.m4.bdi$y ~ mult.pred.m4.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m4.bdi$y by mult.pred.m4.bdi$match
## t = -0.69431, df = 205.76, p-value = 0.4883
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.153497 1.989999
## sample estimates:
## mean in group 0 mean in group 1
## 15.64645 16.72820cohen.d(mult.pred.m4.bdi$y ~ mult.pred.m4.bdi$match)##
## Cohen's d
##
## d estimate: -0.0958433 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.3700256 0.1783390#### Model 5 (Every IV)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(bdi.pai) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(bdi.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(bdi.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = bdi.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:33)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = bdi.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:33)]
X_train_med_data = bdi.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:34)]
X_train_med_enfunc = X_train_med_data[,c(1:33)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = bdi.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:33)]
bdi.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
bdi.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(bdi.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(bdi.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(bdi.cbt.model5, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(bdi.med.model5, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m5.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9252084
## NRN1.1 0.7728312
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.8517168
## NRE2.1 .
## NRE3.1 0.8003363
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7825358
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.4875438
## NRA5.1 1.0304691
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.1301143
## NRC3.1 .
## NRC4.1 0.6631008
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.3602389
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8127010
## NRN1.1 0.7886895
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.5798641
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3925050
## sex .
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.86797024
## NRN1.1 2.29723967
## NRN2.1 0.81325198
## NRN3.1 -1.73176199
## NRN4.1 0.11768105
## NRN5.1 0.89804990
## NRN6.1 -0.38955694
## NRE1.1 -1.65203057
## NRE2.1 1.48398244
## NRE3.1 0.72768818
## NRE4.1 0.09182556
## NRE5.1 0.65568877
## NRE6.1 -1.50938568
## NRO1.1 1.12427980
## NRO2.1 -1.03278781
## NRO3.1 0.19857574
## NRO4.1 0.97547026
## NRO5.1 0.67888044
## NRO6.1 -1.34467338
## NRA1.1 -0.14454091
## NRA2.1 0.66070975
## NRA3.1 1.72204227
## NRA4.1 1.72317485
## NRA5.1 2.44022250
## NRA6.1 -1.90367720
## NRC1.1 0.88941722
## NRC2.1 0.28653233
## NRC3.1 -1.35713030
## NRC4.1 0.57541522
## NRC5.1 -0.12345312
## NRC6.1 0.60101112
## BDI2.1 2.11069132
## sex -0.92746020
## age 1.07596193
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.458326
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.453546
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.3998576
## NRN1.1 2.4029818
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.5755829
## NRE2.1 .
## NRE3.1 0.2844199
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.4128457
## NRO1.1 1.2652301
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.4792728
## NRA3.1 0.4461633
## NRA4.1 1.3700588
## NRA5.1 0.9834656
## NRA6.1 -1.1906080
## NRC1.1 0.4301867
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.9463401
## NRC5.1 0.2447761
## NRC6.1 .
## BDI2.1 1.1965022
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.08194115
## NRN1.1 0.34612072
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.05567126
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.09068746
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.30647236
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.35332883
## NRN1.1 2.05361731
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.11224981
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.08491966
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.80080040
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.15132563
## NRA1.1 .
## NRA2.1 0.62763797
## NRA3.1 .
## NRA4.1 1.33057950
## NRA5.1 1.98209166
## NRA6.1 -0.79380583
## NRC1.1 0.69793958
## NRC2.1 0.15428035
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.95736895
## sex .
## age 0.32663942
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.430454
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.975752
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.83132033
## NRN1.1 1.83596582
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.72505360
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.10533340
## NRA1.1 .
## NRA2.1 0.25469033
## NRA3.1 .
## NRA4.1 1.71641193
## NRA5.1 1.47307733
## NRA6.1 -1.25618641
## NRC1.1 0.06163909
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.61037528
## NRC5.1 0.30171482
## NRC6.1 0.32266039
## BDI2.1 1.20894952
## sex .
## age 0.17500648
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8058335
## NRN1.1 0.5750457
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.1314921
## sex .
## age .
## running cv # 2
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.31125485
## NRN1.1 1.69919761
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.50133958
## NRE2.1 0.05159080
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.98758527
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.99641695
## NRA3.1 .
## NRA4.1 1.15838825
## NRA5.1 0.88406136
## NRA6.1 -0.48255241
## NRC1.1 0.62932078
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.94956269
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.00333402
## sex .
## age 0.04040749
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4609839
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.6483598
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.1561389
## sex .
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.0976697
## NRN1.1 0.7586024
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.4464658
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.4411215
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.2297910
## NRA1.1 .
## NRA2.1 0.2321035
## NRA3.1 .
## NRA4.1 0.7530908
## NRA5.1 1.4889775
## NRA6.1 -0.2228016
## NRC1.1 .
## NRC2.1 0.9105186
## NRC3.1 .
## NRC4.1 0.5228248
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.2498150
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7047556
## NRN1.1 0.4597963
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.1163349
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.1338681
## NRC2.1 .
## NRC3.1 0.4590758
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.1992030
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.2164225
## NRN1.1 1.2407920
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.5368450
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.3933337
## NRO6.1 0.2084428
## NRA1.1 .
## NRA2.1 0.3851479
## NRA3.1 .
## NRA4.1 1.3592568
## NRA5.1 2.3153592
## NRA6.1 -1.0466067
## NRC1.1 1.6208702
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.4516344
## NRC5.1 0.0255479
## NRC6.1 .
## BDI2.1 1.1002897
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8575700
## NRN1.1 0.5468227
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.3380296
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1178736
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3029022
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.28985033
## NRN1.1 3.01204538
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.09589144
## NRE1.1 -0.62110096
## NRE2.1 .
## NRE3.1 0.99098764
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.09422752
## NRO1.1 1.05612880
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.28501109
## NRO5.1 0.34626085
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.75266870
## NRA3.1 1.03773286
## NRA4.1 1.75425944
## NRA5.1 1.10511873
## NRA6.1 -1.85876997
## NRC1.1 .
## NRC2.1 0.00253037
## NRC3.1 .
## NRC4.1 0.38374694
## NRC5.1 1.09263088
## NRC6.1 .
## BDI2.1 1.45370348
## sex -1.06968663
## age 0.17384131
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.5746523
## NRN1.1 0.6922897
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.7620247
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.0483211
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.17007980
## NRN1.1 1.88880578
## NRN2.1 0.30791522
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.30343836
## NRO2.1 -0.01137668
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 0.04750083
## NRA2.1 .
## NRA3.1 .
## NRA4.1 1.76491157
## NRA5.1 1.92820220
## NRA6.1 -1.92612468
## NRC1.1 0.13378964
## NRC2.1 1.08987352
## NRC3.1 .
## NRC4.1 1.13470786
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.51649739
## sex .
## age 0.22614247
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9080174
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.3183433
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.7880025
## sex .
## age .
## running cv # 3
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.1275550
## NRN1.1 1.7637624
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.6789789
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.8687566
## NRA3.1 .
## NRA4.1 0.9457122
## NRA5.1 1.4304605
## NRA6.1 -1.0343477
## NRC1.1 .
## NRC2.1 1.2657996
## NRC3.1 .
## NRC4.1 0.4631723
## NRC5.1 0.1327265
## NRC6.1 .
## BDI2.1 1.2892382
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.704380
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.615254
## sex .
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.92944329
## NRN1.1 3.62703760
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.63168861
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.52252573
## NRE6.1 .
## NRO1.1 1.46693506
## NRO2.1 .
## NRO3.1 0.23491041
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.15340803
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.37063280
## NRA4.1 1.41713207
## NRA5.1 2.43441056
## NRA6.1 -2.30864790
## NRC1.1 0.76240293
## NRC2.1 0.04580215
## NRC3.1 .
## NRC4.1 0.95626016
## NRC5.1 0.34480286
## NRC6.1 .
## BDI2.1 1.20845654
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.1627001
## NRN1.1 0.5631020
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.9164020
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.2533836
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.5336509
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.8260370
## NRN1.1 0.4964640
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.4697772
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.1346718
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.1885220
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.6389209
## NRO5.1 .
## NRO6.1 0.1361320
## NRA1.1 .
## NRA2.1 0.3256701
## NRA3.1 .
## NRA4.1 0.2238199
## NRA5.1 2.0026426
## NRA6.1 -1.1505491
## NRC1.1 0.7406093
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.8336608
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.6113229
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.021941
## NRN1.1 0.464029
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.402929
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.564177
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.19916209
## NRN1.1 1.08117079
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.04199929
## NRE2.1 .
## NRE3.1 1.00906599
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.99004846
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.21179755
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.91401805
## NRA3.1 .
## NRA4.1 1.74391318
## NRA5.1 0.96536532
## NRA6.1 -0.99380233
## NRC1.1 .
## NRC2.1 0.33902010
## NRC3.1 .
## NRC4.1 0.89054015
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.57226814
## sex .
## age 0.75098314
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 18.4282375
## NRN1.1 1.0346165
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.1536751
## NRN5.1 -0.4776167
## NRN6.1 .
## NRE1.1 -1.3931152
## NRE2.1 .
## NRE3.1 2.6844691
## NRE4.1 -1.0876902
## NRE5.1 0.1028373
## NRE6.1 -0.7292252
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.3814946
## NRA1.1 -0.2291593
## NRA2.1 .
## NRA3.1 .
## NRA4.1 -0.2799339
## NRA5.1 .
## NRA6.1 0.0747554
## NRC1.1 2.0522282
## NRC2.1 .
## NRC3.1 2.0361457
## NRC4.1 .
## NRC5.1 -1.6516486
## NRC6.1 0.3658287
## BDI2.1 5.1667820
## sex .
## age -0.4219481
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5108390
## NRN1.1 1.6499398
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.6110940
## NRE2.1 .
## NRE3.1 0.2637511
## NRE4.1 0.1831718
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8317468
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.8831940
## NRA3.1 .
## NRA4.1 1.9214435
## NRA5.1 0.6546021
## NRA6.1 -0.2720711
## NRC1.1 0.5026731
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.4764418
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.4033327
## sex .
## age 0.3101004
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4405797
## NRN1.1 1.4061209
## NRN2.1 0.3138970
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.8118764
## NRE2.1 .
## NRE3.1 0.4252695
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 1.1733467
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.6513353
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.5987973
## NRC2.1 1.6014966
## NRC3.1 0.8307019
## NRC4.1 .
## NRC5.1 -2.7212206
## NRC6.1 .
## BDI2.1 4.0544537
## sex .
## age .
## running cv # 4
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.0239168
## NRN1.1 1.7088903
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.5750898
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7204339
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.8833167
## NRA3.1 0.1361636
## NRA4.1 1.9116002
## NRA5.1 1.0096255
## NRA6.1 -0.7784141
## NRC1.1 .
## NRC2.1 0.5063404
## NRC3.1 .
## NRC4.1 0.6352598
## NRC5.1 0.5084592
## NRC6.1 .
## BDI2.1 1.3701659
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.48838857
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.08957942
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.38827477
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.04471876
## sex .
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.59513287
## NRN1.1 2.83907275
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.59135240
## NRE4.1 0.03618446
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.19450208
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.41080010
## NRA3.1 .
## NRA4.1 0.56171793
## NRA5.1 0.61554445
## NRA6.1 -1.50712047
## NRC1.1 .
## NRC2.1 0.22443663
## NRC3.1 .
## NRC4.1 0.19422100
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.76386432
## sex .
## age 0.26082522
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.12831473
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.59978400
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.02360053
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 0.20290965
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.10037147
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.89871177
## NRN1.1 1.22182994
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.76094144
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.44979230
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.69002279
## NRA3.1 .
## NRA4.1 1.33353028
## NRA5.1 1.78838120
## NRA6.1 -0.90626947
## NRC1.1 0.57761456
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.13897344
## NRC5.1 0.40246191
## NRC6.1 .
## BDI2.1 1.55910220
## sex .
## age 0.07046992
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.11213501
## NRN1.1 0.03531658
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.49500321
## BDI2.1 2.80396466
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.57839905
## NRN1.1 2.14276395
## NRN2.1 .
## NRN3.1 -0.09611961
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.10017596
## NRE1.1 -0.46136825
## NRE2.1 0.51704362
## NRE3.1 0.04257760
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.75208472
## NRO1.1 1.50661190
## NRO2.1 -0.43676463
## NRO3.1 .
## NRO4.1 0.30385272
## NRO5.1 0.14291225
## NRO6.1 .
## NRA1.1 0.06473888
## NRA2.1 0.33091317
## NRA3.1 0.58087472
## NRA4.1 1.59148092
## NRA5.1 1.33517886
## NRA6.1 -1.54444569
## NRC1.1 0.42747919
## NRC2.1 0.51742991
## NRC3.1 .
## NRC4.1 0.68670595
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.10537357
## sex .
## age 0.38505507
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4162444
## NRN1.1 0.3821643
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.3004042
## NRC2.1 .
## NRC3.1 0.1299437
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.8893620
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.30825508
## NRN1.1 0.99166442
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.32677666
## NRN6.1 .
## NRE1.1 -0.66936045
## NRE2.1 0.40276377
## NRE3.1 0.27655278
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.68395128
## NRO2.1 -0.02807941
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.79582125
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.74567032
## NRA5.1 2.09204077
## NRA6.1 -0.84988086
## NRC1.1 1.18163385
## NRC2.1 1.15383364
## NRC3.1 -0.64966688
## NRC4.1 1.31471418
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.20434061
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3054741
## NRN1.1 0.4810861
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.0310117
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3350732
## sex .
## age .
## running cv # 5
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.254011612
## NRN1.1 3.280101494
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.570047287
## NRE2.1 .
## NRE3.1 0.284054460
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.537357947
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.273167008
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.708041275
## NRA3.1 0.007600466
## NRA4.1 0.863166764
## NRA5.1 0.907704876
## NRA6.1 -1.590173429
## NRC1.1 0.462422644
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.773947291
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.683668478
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 13.28304292
## NRN1.1 0.63564318
## NRN2.1 0.03923008
## NRN3.1 .
## NRN4.1 0.64465264
## NRN5.1 -0.08390078
## NRN6.1 .
## NRE1.1 -0.78578234
## NRE2.1 .
## NRE3.1 1.57123134
## NRE4.1 -0.22740089
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.82679280
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.12606159
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.20107508
## NRC2.1 0.75230714
## NRC3.1 1.70959228
## NRC4.1 -0.33852044
## NRC5.1 -1.78587634
## NRC6.1 .
## BDI2.1 2.88962200
## sex 1.60613323
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.94836620
## NRN1.1 1.64311947
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.20519790
## NRE5.1 0.28572319
## NRE6.1 .
## NRO1.1 0.79764877
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.80683773
## NRA3.1 0.01202752
## NRA4.1 1.77231130
## NRA5.1 0.77078187
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.93526328
## NRC5.1 0.30527565
## NRC6.1 .
## BDI2.1 1.18843283
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4150676
## NRN1.1 1.8093413
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.1812826
## NRN6.1 .
## NRE1.1 -0.8820705
## NRE2.1 .
## NRE3.1 1.0866777
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.2502046
## NRC2.1 .
## NRC3.1 0.7074680
## NRC4.1 .
## NRC5.1 -1.8309218
## NRC6.1 .
## BDI2.1 3.6612736
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.366378650
## NRN1.1 1.645953823
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.082074453
## NRN6.1 .
## NRE1.1 -0.252649509
## NRE2.1 0.818510652
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.058629475
## NRE6.1 0.044180341
## NRO1.1 0.714976517
## NRO2.1 -1.143752916
## NRO3.1 .
## NRO4.1 0.198096944
## NRO5.1 .
## NRO6.1 .
## NRA1.1 0.435086199
## NRA2.1 0.621640212
## NRA3.1 .
## NRA4.1 0.648532532
## NRA5.1 2.661629764
## NRA6.1 -1.024242887
## NRC1.1 1.705032888
## NRC2.1 0.048541806
## NRC3.1 -0.849848289
## NRC4.1 1.005753650
## NRC5.1 0.002211822
## NRC6.1 0.400927099
## BDI2.1 2.237241036
## sex -1.548769514
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.3245631
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1640365
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.2628945
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.1056197
## NRN1.1 0.3373200
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.2405371
## NRA1.1 .
## NRA2.1 0.1634774
## NRA3.1 .
## NRA4.1 0.5726718
## NRA5.1 1.2044452
## NRA6.1 .
## NRC1.1 .
## NRC2.1 1.7132059
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.6591253
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.98821423
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.46372347
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.03787967
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.35985296
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.264628339
## NRN1.1 1.672545132
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.307340169
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.922944738
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.004512122
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.845061320
## NRA3.1 .
## NRA4.1 1.473375894
## NRA5.1 1.009443827
## NRA6.1 -1.197303686
## NRC1.1 .
## NRC2.1 0.636225845
## NRC3.1 .
## NRC4.1 0.487084464
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.337044847
## sex .
## age 0.746385570
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.8398495
## NRN1.1 0.3914548
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.5078744
## NRC2.1 0.1604107
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.4116525
## sex .
## age .
## running cv # 6
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.9767982
## NRN1.1 1.1318083
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.3800206
## NRE1.1 -0.7454030
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.2670096
## NRO2.1 -0.4359308
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.5147142
## NRA3.1 .
## NRA4.1 1.3363341
## NRA5.1 1.7128032
## NRA6.1 -0.5959680
## NRC1.1 .
## NRC2.1 0.3779659
## NRC3.1 .
## NRC4.1 0.9848161
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.0422242
## sex .
## age 0.3161810
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.73402822
## NRN1.1 1.10395176
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -1.22683520
## NRN6.1 0.02832036
## NRE1.1 -2.27599693
## NRE2.1 .
## NRE3.1 2.13765609
## NRE4.1 -1.15232789
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.79044789
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.16114001
## NRO5.1 -0.19946644
## NRO6.1 .
## NRA1.1 -0.13624126
## NRA2.1 0.05095895
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.53301230
## NRA6.1 .
## NRC1.1 1.24281248
## NRC2.1 0.44269582
## NRC3.1 1.06291098
## NRC4.1 .
## NRC5.1 -2.40417426
## NRC6.1 .
## BDI2.1 4.01399902
## sex 0.31462100
## age -0.34488966
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.2939692
## NRN1.1 1.8355689
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.2300528
## NRE2.1 0.0608521
## NRE3.1 0.3784873
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.5038782
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.5974566
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.4110007
## NRA3.1 0.2340653
## NRA4.1 1.9965944
## NRA5.1 1.9444397
## NRA6.1 -1.4877341
## NRC1.1 0.3112024
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.5118415
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.5560395
## sex .
## age 0.1572816
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.93426827
## NRN1.1 0.48766243
## NRN2.1 .
## NRN3.1 0.13165166
## NRN4.1 0.48210520
## NRN5.1 -0.32446102
## NRN6.1 .
## NRE1.1 -1.73262688
## NRE2.1 .
## NRE3.1 2.12773003
## NRE4.1 -0.00455427
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.19556349
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.40228926
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.11629383
## NRA2.1 0.72300430
## NRA3.1 0.45824081
## NRA4.1 -1.30228491
## NRA5.1 0.03235563
## NRA6.1 0.82543095
## NRC1.1 2.89351340
## NRC2.1 1.63147380
## NRC3.1 0.69910115
## NRC4.1 .
## NRC5.1 -3.59980077
## NRC6.1 .
## BDI2.1 3.72122045
## sex .
## age -0.68614390
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5727972
## NRN1.1 3.2074321
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.2530594
## NRE2.1 .
## NRE3.1 0.2306265
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.4966987
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.2240610
## NRO5.1 0.2648597
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.0180257
## NRA3.1 0.9752145
## NRA4.1 1.6123779
## NRA5.1 0.8083869
## NRA6.1 -2.4485739
## NRC1.1 .
## NRC2.1 0.4652845
## NRC3.1 .
## NRC4.1 0.2330307
## NRC5.1 0.6418227
## NRC6.1 .
## BDI2.1 1.2410914
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.43975026
## NRN1.1 0.65691937
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.03165842
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.63497017
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.50592730
## NRN1.1 1.32215952
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.08140353
## NRE2.1 .
## NRE3.1 0.70906004
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.47959297
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.60365710
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.57634741
## NRA5.1 2.12835579
## NRA6.1 -1.30529013
## NRC1.1 1.59595841
## NRC2.1 0.30445975
## NRC3.1 -0.58143195
## NRC4.1 1.58297136
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.22116264
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.870565
## NRN1.1 1.236142
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.262148
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.008312
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.9484586
## NRN1.1 1.6074323
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.3479877
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.1180380
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.2467511
## NRA3.1 .
## NRA4.1 1.0656058
## NRA5.1 1.1477100
## NRA6.1 -0.3991366
## NRC1.1 0.4576820
## NRC2.1 0.4943219
## NRC3.1 .
## NRC4.1 0.7797851
## NRC5.1 0.2550731
## NRC6.1 .
## BDI2.1 1.6323428
## sex .
## age 0.2505889
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.04700447
## NRN1.1 0.04516116
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.30529797
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.18883483
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.77988288
## NRC2.1 .
## NRC3.1 0.32002961
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 6.12450470
## sex .
## age .
## running cv # 7
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.2380469
## NRN1.1 0.7472172
## NRN2.1 .
## NRN3.1 0.1412189
## NRN4.1 .
## NRN5.1 0.0259709
## NRN6.1 .
## NRE1.1 -0.8334676
## NRE2.1 1.0592033
## NRE3.1 0.3300277
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.2466859
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.5562932
## NRO5.1 .
## NRO6.1 0.2625900
## NRA1.1 .
## NRA2.1 0.4412895
## NRA3.1 .
## NRA4.1 0.6656239
## NRA5.1 1.9776506
## NRA6.1 -0.9831492
## NRC1.1 0.8483294
## NRC2.1 0.7485454
## NRC3.1 .
## NRC4.1 0.5858563
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.8450995
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.9814495
## NRN1.1 0.1733962
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1666611
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.8367993
## sex .
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5257718
## NRN1.1 1.7649676
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8564828
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.1443971
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.5406769
## NRA3.1 .
## NRA4.1 1.8237297
## NRA5.1 1.8536568
## NRA6.1 -1.5372723
## NRC1.1 .
## NRC2.1 1.0208127
## NRC3.1 .
## NRC4.1 1.1254087
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.0052060
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7346162
## NRN1.1 1.0403271
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2737689
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.6919527
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.9691069
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7508287
## NRN1.1 2.5047450
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 0.1659896
## NRE1.1 -1.3645523
## NRE2.1 0.7299935
## NRE3.1 0.5930418
## NRE4.1 1.1939133
## NRE5.1 .
## NRE6.1 -0.3505632
## NRO1.1 1.6764991
## NRO2.1 -0.7536930
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.5482055
## NRA3.1 0.7977639
## NRA4.1 1.9130227
## NRA5.1 1.1635062
## NRA6.1 -0.9523452
## NRC1.1 0.6576698
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.5652281
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.0001485
## sex -0.5262706
## age 0.6328906
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.33531489
## NRN1.1 0.10922333
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -1.04750616
## NRN6.1 .
## NRE1.1 -2.62530528
## NRE2.1 .
## NRE3.1 1.88392099
## NRE4.1 -1.62044206
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.25046299
## NRO2.1 0.01511224
## NRO3.1 .
## NRO4.1 -0.19853336
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.16891999
## NRA3.1 .
## NRA4.1 0.23834860
## NRA5.1 .
## NRA6.1 0.10649675
## NRC1.1 1.38841652
## NRC2.1 1.10184435
## NRC3.1 1.13742842
## NRC4.1 .
## NRC5.1 -1.99949179
## NRC6.1 .
## BDI2.1 3.90924636
## sex 0.18793168
## age -1.29143806
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.7334744
## NRN1.1 1.6593249
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.1240366
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.7183553
## NRA1.1 .
## NRA2.1 0.7297539
## NRA3.1 .
## NRA4.1 0.9608700
## NRA5.1 1.8330071
## NRA6.1 -0.9311376
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.4191395
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.8255940
## sex .
## age 0.1204693
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.26689946
## NRN1.1 1.04778082
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.02426164
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 1.57426449
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 -0.08669631
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.54986865
## NRC2.1 .
## NRC3.1 0.39270571
## NRC4.1 .
## NRC5.1 -0.06270880
## NRC6.1 0.10541780
## BDI2.1 3.96366942
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.6045473
## NRN1.1 1.4720196
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.8124948
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.6650343
## NRA3.1 .
## NRA4.1 0.7814488
## NRA5.1 0.1014447
## NRA6.1 -0.3809257
## NRC1.1 0.3140409
## NRC2.1 0.2249548
## NRC3.1 .
## NRC4.1 0.3644647
## NRC5.1 0.1802277
## NRC6.1 .
## BDI2.1 1.4532390
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.9155327
## NRN1.1 0.2112605
## NRN2.1 .
## NRN3.1 0.3367401
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.4062294
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.0188647
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 -0.8558329
## NRC6.1 0.3488439
## BDI2.1 3.5685034
## sex .
## age .
## running cv # 8
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.04177150
## NRN1.1 1.12083389
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.63525841
## NRN6.1 .
## NRE1.1 -1.35872361
## NRE2.1 0.03324979
## NRE3.1 0.67117234
## NRE4.1 .
## NRE5.1 0.16452512
## NRE6.1 .
## NRO1.1 1.69502269
## NRO2.1 -0.19297699
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.02348112
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.02111978
## NRA3.1 0.67996368
## NRA4.1 1.75499234
## NRA5.1 1.28524539
## NRA6.1 -0.26568390
## NRC1.1 .
## NRC2.1 0.49956182
## NRC3.1 .
## NRC4.1 0.90208126
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.69299285
## sex -0.82371484
## age 1.20227595
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.76749816
## NRN1.1 0.34084791
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.44637143
## NRN6.1 .
## NRE1.1 -0.68035689
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.42122613
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.08217311
## BDI2.1 4.15374919
## sex .
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.7953245
## NRN1.1 2.5252262
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.2968158
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.7086829
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.1193792
## NRA1.1 .
## NRA2.1 0.4762251
## NRA3.1 0.1926998
## NRA4.1 1.0173502
## NRA5.1 0.6368318
## NRA6.1 -1.3679452
## NRC1.1 .
## NRC2.1 0.2632434
## NRC3.1 .
## NRC4.1 0.7330328
## NRC5.1 0.4770080
## NRC6.1 .
## BDI2.1 1.4713337
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.1525886
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.4724092
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.7345665
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.5341989
## NRN1.1 1.6345465
## NRN2.1 0.3653548
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1522388
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.0643965
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.3567907
## NRO5.1 .
## NRO6.1 0.1100625
## NRA1.1 .
## NRA2.1 0.3042537
## NRA3.1 .
## NRA4.1 1.9187528
## NRA5.1 1.8696053
## NRA6.1 -1.0262552
## NRC1.1 0.6882482
## NRC2.1 0.3462655
## NRC3.1 .
## NRC4.1 0.9473774
## NRC5.1 .
## NRC6.1 0.2539728
## BDI2.1 2.0800133
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.34418732
## NRN1.1 1.53638083
## NRN2.1 0.60019993
## NRN3.1 0.16124079
## NRN4.1 0.01695899
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.33464542
## NRE2.1 .
## NRE3.1 1.78042046
## NRE4.1 -1.18307358
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.52039836
## NRO2.1 0.39805905
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.67281652
## NRC2.1 0.77548376
## NRC3.1 0.76103505
## NRC4.1 .
## NRC5.1 -1.35517493
## NRC6.1 .
## BDI2.1 3.81183457
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.40740643
## NRN1.1 1.99319280
## NRN2.1 .
## NRN3.1 -0.20212148
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.65286615
## NRE2.1 0.03534234
## NRE3.1 .
## NRE4.1 .
## NRE5.1 0.70193477
## NRE6.1 -0.31172695
## NRO1.1 0.37550701
## NRO2.1 -0.07620252
## NRO3.1 .
## NRO4.1 0.06469974
## NRO5.1 .
## NRO6.1 0.19109183
## NRA1.1 .
## NRA2.1 0.92726584
## NRA3.1 0.44451009
## NRA4.1 0.88224629
## NRA5.1 2.48688977
## NRA6.1 -2.05276326
## NRC1.1 1.74535136
## NRC2.1 .
## NRC3.1 -0.41982127
## NRC4.1 0.94599896
## NRC5.1 0.14833284
## NRC6.1 .
## BDI2.1 2.23857896
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4710780
## NRN1.1 0.2878932
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.1513801
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.4116977
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.06023669
## NRN1.1 1.36908478
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 0.03327617
## NRE3.1 .
## NRE4.1 0.59213390
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.69180840
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.78491463
## NRA3.1 .
## NRA4.1 1.25170167
## NRA5.1 1.34219240
## NRA6.1 -0.84434466
## NRC1.1 .
## NRC2.1 0.02635489
## NRC3.1 .
## NRC4.1 0.41654319
## NRC5.1 0.56478229
## NRC6.1 .
## BDI2.1 0.34938699
## sex .
## age 0.47231277
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.80617582
## NRN1.1 0.76030475
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 0.03716092
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 2.98281405
## sex .
## age .
## running cv # 9
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.35464829
## NRN1.1 1.26110488
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.26167691
## NRE2.1 0.41136108
## NRE3.1 0.18515777
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.63806802
## NRO2.1 -0.63472388
## NRO3.1 -0.17745209
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.08721631
## NRA3.1 .
## NRA4.1 0.36621111
## NRA5.1 2.42010072
## NRA6.1 -0.16035139
## NRC1.1 1.73453527
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.93307449
## NRC5.1 .
## NRC6.1 0.23857342
## BDI2.1 1.86257627
## sex -0.10382582
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.17977772
## NRN1.1 1.58827264
## NRN2.1 1.04246048
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.26969360
## NRN6.1 .
## NRE1.1 -0.69773522
## NRE2.1 .
## NRE3.1 1.33637680
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 0.24095623
## NRO4.1 -0.04018932
## NRO5.1 -0.76585629
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 -0.03919382
## NRA5.1 .
## NRA6.1 0.19851648
## NRC1.1 2.53183376
## NRC2.1 1.43421685
## NRC3.1 1.29212229
## NRC4.1 .
## NRC5.1 -2.81559772
## NRC6.1 .
## BDI2.1 2.64471531
## sex .
## age -0.19787296
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.64327702
## NRN1.1 2.17763734
## NRN2.1 1.40965787
## NRN3.1 -1.41175150
## NRN4.1 0.67878729
## NRN5.1 0.05543399
## NRN6.1 -0.26736275
## NRE1.1 -1.16936315
## NRE2.1 1.21694167
## NRE3.1 0.31915686
## NRE4.1 0.90427087
## NRE5.1 0.42950653
## NRE6.1 -1.05375741
## NRO1.1 1.36855055
## NRO2.1 -0.90054474
## NRO3.1 0.55407051
## NRO4.1 0.28460678
## NRO5.1 0.16847557
## NRO6.1 -0.59570589
## NRA1.1 0.28891025
## NRA2.1 0.91371771
## NRA3.1 1.18814214
## NRA4.1 1.76005877
## NRA5.1 2.10985346
## NRA6.1 -1.97518609
## NRC1.1 0.99472341
## NRC2.1 0.20379375
## NRC3.1 -1.57940992
## NRC4.1 0.63289842
## NRC5.1 0.68354397
## NRC6.1 0.57160997
## BDI2.1 2.26012481
## sex -1.23470025
## age 0.67999101
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.4918662
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.1890681
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.3431325
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.4159389
## NRN1.1 1.6007958
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.9922048
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.3058527
## NRA3.1 .
## NRA4.1 1.0502156
## NRA5.1 2.5588135
## NRA6.1 -0.1946484
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.2444567
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.6228095
## NRN1.1 1.0493460
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.1346331
## NRN6.1 .
## NRE1.1 -0.8645037
## NRE2.1 .
## NRE3.1 1.5287343
## NRE4.1 -0.7979240
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.4227204
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.0072808
## NRC2.1 .
## NRC3.1 1.3899062
## NRC4.1 .
## NRC5.1 -1.1464610
## NRC6.1 .
## BDI2.1 4.2242861
## sex .
## age -0.3789821
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.7339507
## NRN1.1 2.3132117
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.4755933
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 0.2867879
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.6803166
## NRA3.1 .
## NRA4.1 2.4011966
## NRA5.1 0.5631106
## NRA6.1 -1.4273168
## NRC1.1 .
## NRC2.1 0.4877236
## NRC3.1 .
## NRC4.1 0.7795655
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.9437668
## sex .
## age 0.5775304
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.53708697
## NRN1.1 0.22823107
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -1.63439447
## NRN6.1 .
## NRE1.1 -0.64641337
## NRE2.1 .
## NRE3.1 0.94302070
## NRE4.1 -1.55404261
## NRE5.1 0.58650632
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.19876688
## NRA2.1 1.18724048
## NRA3.1 0.21845266
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 2.00656819
## NRC2.1 0.67884262
## NRC3.1 0.09336809
## NRC4.1 .
## NRC5.1 -1.42252249
## NRC6.1 0.41564613
## BDI2.1 5.40779653
## sex .
## age -0.03484895
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.23759806
## NRN1.1 1.29321531
## NRN2.1 0.06857436
## NRN3.1 -0.50126994
## NRN4.1 0.26402014
## NRN5.1 0.75740035
## NRN6.1 1.20678598
## NRE1.1 -1.52078162
## NRE2.1 0.47068339
## NRE3.1 0.54109478
## NRE4.1 0.89132564
## NRE5.1 1.35229317
## NRE6.1 -1.53346821
## NRO1.1 2.17892025
## NRO2.1 -0.95734365
## NRO3.1 -0.26608729
## NRO4.1 -0.46235372
## NRO5.1 -0.31161719
## NRO6.1 0.34020409
## NRA1.1 -0.09473784
## NRA2.1 0.31948364
## NRA3.1 0.79559107
## NRA4.1 1.63091599
## NRA5.1 1.82736027
## NRA6.1 -1.66217607
## NRC1.1 2.15774486
## NRC2.1 0.93654702
## NRC3.1 -2.11971201
## NRC4.1 1.47542296
## NRC5.1 -0.56637816
## NRC6.1 0.22708667
## BDI2.1 2.43961793
## sex 0.85001883
## age 1.06319604
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.65197182
## NRN1.1 0.15868672
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.05289299
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.18156980
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.94693566
## sex .
## age .
## running cv # 10
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.21309280
## NRN1.1 1.71055909
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.21091626
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.50494548
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.42958061
## NRA1.1 .
## NRA2.1 0.64259851
## NRA3.1 .
## NRA4.1 0.92776722
## NRA5.1 1.48083696
## NRA6.1 -0.97466970
## NRC1.1 0.06985192
## NRC2.1 .
## NRC3.1 .
## NRC4.1 0.87420756
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.64523593
## sex .
## age 0.21684376
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.315088
## NRN1.1 1.277139
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.947197
## sex .
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 16.7443518
## NRN1.1 2.1143186
## NRN2.1 0.4825810
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 0.4771120
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.2194355
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.2381569
## NRA3.1 .
## NRA4.1 1.7480714
## NRA5.1 1.2378766
## NRA6.1 -1.0862458
## NRC1.1 .
## NRC2.1 1.0129984
## NRC3.1 .
## NRC4.1 0.7672364
## NRC5.1 .
## NRC6.1 .
## BDI2.1 1.6813205
## sex -0.7271981
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.2807882
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.4691928
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 1.4164503
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.6423045
## sex .
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 14.54541130
## NRN1.1 0.71743734
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.19513409
## NRN6.1 .
## NRE1.1 -1.10103811
## NRE2.1 0.60454703
## NRE3.1 0.70858663
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.83556829
## NRO2.1 -0.63514016
## NRO3.1 .
## NRO4.1 0.66980029
## NRO5.1 0.68407359
## NRO6.1 .
## NRA1.1 .
## NRA2.1 1.15882863
## NRA3.1 0.79457739
## NRA4.1 .
## NRA5.1 1.85765534
## NRA6.1 -0.70810543
## NRC1.1 0.36914386
## NRC2.1 0.86192981
## NRC3.1 .
## NRC4.1 0.04903195
## NRC5.1 0.04662718
## NRC6.1 .
## BDI2.1 1.94740194
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.65767107
## NRN1.1 0.03852383
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.53445012
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 4.06858808
## sex .
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.72863032
## NRN1.1 2.12754214
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 1.07640615
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 0.01622117
## NRA1.1 .
## NRA2.1 0.40255083
## NRA3.1 .
## NRA4.1 1.48138652
## NRA5.1 1.56420636
## NRA6.1 -1.43881521
## NRC1.1 0.72979452
## NRC2.1 0.14498397
## NRC3.1 .
## NRC4.1 0.48017594
## NRC5.1 .
## NRC6.1 .
## BDI2.1 0.66570859
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 17.4449606
## NRN1.1 1.2061180
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 -0.6466136
## NRN6.1 .
## NRE1.1 -1.6382810
## NRE2.1 .
## NRE3.1 2.5756905
## NRE4.1 -1.3302176
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.4085066
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.2988189
## NRC1.1 0.2806403
## NRC2.1 .
## NRC3.1 1.1493524
## NRC4.1 .
## NRC5.1 -0.8563691
## NRC6.1 .
## BDI2.1 4.0654835
## sex .
## age -1.1154203
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.58747480
## NRN1.1 2.12787580
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 0.25408520
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.24816352
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 0.65481892
## NRA3.1 .
## NRA4.1 2.04452220
## NRA5.1 1.22979828
## NRA6.1 -1.06480009
## NRC1.1 1.40025722
## NRC2.1 .
## NRC3.1 .
## NRC4.1 1.23766737
## NRC5.1 0.07795929
## NRC6.1 .
## BDI2.1 1.39248751
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 15.72074755
## NRN1.1 0.02620021
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 0.05922291
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## BDI2.1 3.41884693
## sex .
## age .mult.pred.m5.bdi = as.data.frame(mult.pred.m5.bdi)
mult.pred.m5.bdi$mean = rowMeans(cbind(mult.pred.m5.bdi$V1, mult.pred.m5.bdi$V2, mult.pred.m5.bdi$V3, mult.pred.m5.bdi$V4, mult.pred.m5.bdi$V5,
mult.pred.m5.bdi$V6, mult.pred.m5.bdi$V7, mult.pred.m5.bdi$V8, mult.pred.m5.bdi$V9, mult.pred.m5.bdi$V10))
describe(abs(mult.pred.m5.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 3.56 2.58 2.98 3.31 2.6 0.02 12.06 12.05 0.81 0.05
## se Q0.25 Q0.75
## 1 0.18 1.41 5.14mult.pred.m5.bdi = cbind(mult.pred.m5.bdi, bdi.pai)
mult.pred.m5.bdi$indication = if_else(mult.pred.m5.bdi$mean < 0, 0, 1)
cbt.indicated.m5.bdi = filter(mult.pred.m5.bdi, indication == 0)
med.indicated.m5.bdi = filter(mult.pred.m5.bdi, indication == 1)
# Analyses across tx group
mult.pred.m5.bdi$match = if_else(mult.pred.m5.bdi$indication==mult.pred.m5.bdi$txgrp, 0, 1)
describeBy(mult.pred.m5.bdi$y, group = mult.pred.m5.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 97 15.67 10.66 14 14.76 10.38 0 47 47 0.78 0.34
## se Q0.25 Q0.75
## 1 1.08 8 22
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 111 16.71 11.81 14 15.84 11.86 0 51 51 0.64 -0.41
## se Q0.25 Q0.75
## 1 1.12 7 25t.test(mult.pred.m5.bdi$y ~ mult.pred.m5.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m5.bdi$y by mult.pred.m5.bdi$match
## t = -0.66948, df = 205.77, p-value = 0.5039
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.114973 2.028748
## sample estimates:
## mean in group 0 mean in group 1
## 15.66707 16.71018cohen.d(mult.pred.m5.bdi$y ~ mult.pred.m5.bdi$match)##
## Cohen's d
##
## d estimate: -0.0924126 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.3665839 0.1817587#### Model 6 (Pre-tx BDI-II only)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.bdi) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.bdi$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.bdi$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.bdi[cbt_indexnums_train,]
X_train_cbt_data = select(X_train_cbt_data, BDI2.1, BDI2.2)
X_train_cbt_data = rename(X_train_cbt_data, y = BDI2.2)
X_test_cbt = neo.depr.bdi[cbt_indexnums_test,]
X_test_cbt = select(X_test_cbt, BDI2.1, BDI2.2)
X_test_cbt = rename(X_test_cbt, y = BDI2.2)
X_train_med_data = neo.depr.bdi[med_indexnums_train,]
X_train_med_data = select(X_train_med_data, BDI2.1, BDI2.2)
X_train_med_data = rename(X_train_med_data, y = BDI2.2)
X_test_med = neo.depr.bdi[med_indexnums_test,]
X_test_med = select(X_test_med, BDI2.1, BDI2.2)
X_test_med = rename(X_test_med, y = BDI2.2)
bdi.cbt.train.model = regression.pai(data = X_train_cbt_data)
bdi.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(bdi.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(bdi.med.train.model, X_test_med)
cbtpred_medgrp = predict(bdi.cbt.lm6, X_test_med)
medpred_cbtgrp = predict(bdi.med.lm6, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm.bdi = as.data.frame(mult.pred.lm.bdi)
mult.pred.lm.bdi$mean = rowMeans(cbind(mult.pred.lm.bdi$V1, mult.pred.lm.bdi$V2, mult.pred.lm.bdi$V3, mult.pred.lm.bdi$V4, mult.pred.lm.bdi$V5,
mult.pred.lm.bdi$V6, mult.pred.lm.bdi$V7, mult.pred.lm.bdi$V8, mult.pred.lm.bdi$V9, mult.pred.lm.bdi$V10))
describe(abs(mult.pred.lm.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 1.52 1.14 1.31 1.39 0.91 0.01 7.01 7 1.48 3.4
## se Q0.25 Q0.75
## 1 0.08 0.76 2.01mult.pred.lm.bdi = cbind(mult.pred.lm.bdi, neo.depr.bdi)
mult.pred.lm.bdi$indication = if_else(mult.pred.lm.bdi$mean < 0, 0, 1)
cbt.indicated.lm.bdi = filter(mult.pred.lm.bdi, indication == 0)
med.indicated.lm.bdi = filter(mult.pred.lm.bdi, indication == 1)
# Analyses across tx group
mult.pred.lm.bdi$match = if_else(mult.pred.lm.bdi$indication==mult.pred.lm.bdi$txgrp, 0, 1)
describeBy(mult.pred.lm.bdi$BDI2.2, group = mult.pred.lm.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 93 15.2 9.78 14 14.49 10.38 0 44 44 0.62 0.06 1.01
## Q0.25 Q0.75
## 1 8 21
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 115 17.05 12.33 14 16.12 11.86 0 51 51 0.66 -0.42
## se Q0.25 Q0.75
## 1 1.15 7 26t.test(mult.pred.lm.bdi$BDI2.2 ~ mult.pred.lm.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm.bdi$BDI2.2 by mult.pred.lm.bdi$match
## t = -1.209, df = 205.93, p-value = 0.2281
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.875846 1.169087
## sample estimates:
## mean in group 0 mean in group 1
## 15.19902 17.05240cohen.d(mult.pred.lm.bdi$BDI2.2 ~ mult.pred.lm.bdi$match)##
## Cohen's d
##
## d estimate: -0.1645743 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.4399807 0.1108321######### Ordinary least squares versions of the above models ##########
#### Model 1 (Pre-tx BDI-II and demographics)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.bdi) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.bdi$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.bdi$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.bdi[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(31, 32, 35, 36)]
X_train_cbt_data = rename(X_train_cbt_data, y = BDI2.2)
X_test_cbt = neo.depr.bdi[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(31, 32, 35, 36)]
X_test_cbt = rename(X_test_cbt, y = BDI2.2)
X_train_med_data = neo.depr.bdi[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(31, 32, 35, 36)]
X_train_med_data = rename(X_train_med_data, y = BDI2.2)
X_test_med = neo.depr.bdi[med_indexnums_test,]
X_test_med = X_test_med[,c(31, 32, 35, 36)]
X_test_med = rename(X_test_med, y = BDI2.2)
bdi.cbt.train.model = regression.pai(data = X_train_cbt_data)
bdi.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(bdi.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(bdi.med.train.model, X_test_med)
cbtpred_medgrp = predict(bdi.cbt.lm1, X_test_med)
medpred_cbtgrp = predict(bdi.med.lm1, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm1.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm1.bdi = as.data.frame(mult.pred.lm1.bdi)
mult.pred.lm1.bdi$mean = rowMeans(cbind(mult.pred.lm1.bdi$V1, mult.pred.lm1.bdi$V2, mult.pred.lm1.bdi$V3, mult.pred.lm1.bdi$V4, mult.pred.lm1.bdi$V5,
mult.pred.lm1.bdi$V6, mult.pred.lm1.bdi$V7, mult.pred.lm1.bdi$V8, mult.pred.lm1.bdi$V9, mult.pred.lm1.bdi$V10))
describe(abs(mult.pred.lm1.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 2.58 1.83 2.23 2.4 1.92 0.01 9.38 9.38 0.92 0.78
## se Q0.25 Q0.75
## 1 0.13 1.09 3.79mult.pred.lm1.bdi = cbind(mult.pred.lm1.bdi, neo.depr.bdi)
mult.pred.lm1.bdi$indication = if_else(mult.pred.lm1.bdi$mean < 0, 0, 1)
cbt.indicated.lm1.bdi = filter(mult.pred.lm1.bdi, indication == 0)
med.indicated.lm1.bdi = filter(mult.pred.lm1.bdi, indication == 1)
# Analyses across tx group
mult.pred.lm1.bdi$match = if_else(mult.pred.lm1.bdi$indication==mult.pred.lm1.bdi$txgrp, 0, 1)
describeBy(mult.pred.lm1.bdi$BDI2.2, group = mult.pred.lm1.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 102 15.37 9.39 14 14.87 9.64 0 46 46 0.59 0.27 0.93
## Q0.25 Q0.75
## 1 10 21
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 106 17.05 12.82 13 16.09 11.86 0 51 51 0.64 -0.61
## se Q0.25 Q0.75
## 1 1.24 7 27.5t.test(mult.pred.lm1.bdi$BDI2.2 ~ mult.pred.lm1.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm1.bdi$BDI2.2 by mult.pred.lm1.bdi$match
## t = -1.081, df = 192.57, p-value = 0.2811
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.744394 1.385032
## sample estimates:
## mean in group 0 mean in group 1
## 15.36774 17.04742cohen.d(mult.pred.lm1.bdi$BDI2.2 ~ mult.pred.lm1.bdi$match)##
## Cohen's d
##
## d estimate: -0.1490649 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.4228992 0.1247693#### Model 2 (FFM facets only)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.bdi) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.bdi$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.bdi$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.bdi[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 32)]
X_train_cbt_data = rename(X_train_cbt_data, y = BDI2.2)
X_test_cbt = neo.depr.bdi[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30, 32)]
X_test_cbt = rename(X_test_cbt, y = BDI2.2)
X_train_med_data = neo.depr.bdi[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 32)]
X_train_med_data = rename(X_train_med_data, y = BDI2.2)
X_test_med = neo.depr.bdi[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30, 32)]
X_test_med = rename(X_test_med, y = BDI2.2)
bdi.cbt.train.model = regression.pai(data = X_train_cbt_data)
bdi.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(bdi.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(bdi.med.train.model, X_test_med)
cbtpred_medgrp = predict(bdi.cbt.lm2, X_test_med)
medpred_cbtgrp = predict(bdi.med.lm2, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm2.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm2.bdi = as.data.frame(mult.pred.lm2.bdi)
mult.pred.lm2.bdi$mean = rowMeans(cbind(mult.pred.lm2.bdi$V1, mult.pred.lm2.bdi$V2, mult.pred.lm2.bdi$V3, mult.pred.lm2.bdi$V4, mult.pred.lm2.bdi$V5,
mult.pred.lm2.bdi$V6, mult.pred.lm2.bdi$V7, mult.pred.lm2.bdi$V8, mult.pred.lm2.bdi$V9, mult.pred.lm2.bdi$V10))
describe(abs(mult.pred.lm2.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 6.95 5.93 5.27 6.14 4.77 0.05 38.87 38.82 1.6 3.91
## se Q0.25 Q0.75
## 1 0.41 2.7 9.92mult.pred.lm2.bdi = cbind(mult.pred.lm2.bdi, neo.depr.bdi)
mult.pred.lm2.bdi$indication = if_else(mult.pred.lm2.bdi$mean < 0, 0, 1)
cbt.indicated.lm2.bdi = filter(mult.pred.lm2.bdi, indication == 0)
med.indicated.lm2.bdi = filter(mult.pred.lm2.bdi, indication == 1)
# Analyses across tx group
mult.pred.lm2.bdi$match = if_else(mult.pred.lm2.bdi$indication==mult.pred.lm2.bdi$txgrp, 0, 1)
describeBy(mult.pred.lm2.bdi$BDI2.2, group = mult.pred.lm2.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 109 16.69 11.01 14 15.94 10.38 0 47 47 0.66 -0.11
## se Q0.25 Q0.75
## 1 1.05 10 22
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 99 15.71 11.58 14 14.71 11.86 0 51 51 0.77 -0.06
## se Q0.25 Q0.75
## 1 1.16 6 23t.test(mult.pred.lm2.bdi$BDI2.2 ~ mult.pred.lm2.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm2.bdi$BDI2.2 by mult.pred.lm2.bdi$match
## t = 0.62961, df = 201.64, p-value = 0.5297
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.108723 4.087109
## sample estimates:
## mean in group 0 mean in group 1
## 16.69454 15.70535cohen.d(mult.pred.lm2.bdi$BDI2.2 ~ mult.pred.lm2.bdi$match)##
## Cohen's d
##
## d estimate: 0.08762647 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.1862254 0.3614783#### Model 3 (Demographics and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.bdi) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.bdi$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.bdi$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.bdi[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 32, 35, 36)]
X_train_cbt_data = rename(X_train_cbt_data, y = BDI2.2)
X_test_cbt = neo.depr.bdi[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30, 32, 35, 36)]
X_test_cbt = rename(X_test_cbt, y = BDI2.2)
X_train_med_data = neo.depr.bdi[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 32, 35, 36)]
X_train_med_data = rename(X_train_med_data, y = BDI2.2)
X_test_med = neo.depr.bdi[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30, 32, 35, 36)]
X_test_med = rename(X_test_med, y = BDI2.2)
bdi.cbt.train.model = regression.pai(data = X_train_cbt_data)
bdi.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(bdi.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(bdi.med.train.model, X_test_med)
cbtpred_medgrp = predict(bdi.cbt.lm3, X_test_med)
medpred_cbtgrp = predict(bdi.med.lm3, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm3.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm3.bdi = as.data.frame(mult.pred.lm3.bdi)
mult.pred.lm3.bdi$mean = rowMeans(cbind(mult.pred.lm3.bdi$V1, mult.pred.lm3.bdi$V2, mult.pred.lm3.bdi$V3, mult.pred.lm3.bdi$V4, mult.pred.lm3.bdi$V5,
mult.pred.lm3.bdi$V6, mult.pred.lm3.bdi$V7, mult.pred.lm3.bdi$V8, mult.pred.lm3.bdi$V9, mult.pred.lm3.bdi$V10))
describe(abs(mult.pred.lm3.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 7.51 6.13 6.06 6.72 5.45 0.14 39.56 39.42 1.44 3.21
## se Q0.25 Q0.75
## 1 0.43 2.81 10.62mult.pred.lm3.bdi = cbind(mult.pred.lm3.bdi, neo.depr.bdi)
mult.pred.lm3.bdi$indication = if_else(mult.pred.lm3.bdi$mean < 0, 0, 1)
cbt.indicated.lm3.bdi = filter(mult.pred.lm3.bdi, indication == 0)
med.indicated.lm3.bdi = filter(mult.pred.lm3.bdi, indication == 1)
# Analyses across tx group
mult.pred.lm3.bdi$match = if_else(mult.pred.lm3.bdi$indication==mult.pred.lm3.bdi$txgrp, 0, 1)
describeBy(mult.pred.lm3.bdi$BDI2.2, group = mult.pred.lm3.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 108 16.67 11.45 14 15.75 10.38 0 51 51 0.78 0.18
## se Q0.25 Q0.75
## 1 1.1 8.75 22.25
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 100 15.74 11.11 14 14.85 11.86 0 44 44 0.63 -0.49
## se Q0.25 Q0.75
## 1 1.11 6.75 23t.test(mult.pred.lm3.bdi$BDI2.2 ~ mult.pred.lm3.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm3.bdi$BDI2.2 by mult.pred.lm3.bdi$match
## t = 0.5974, df = 205.55, p-value = 0.5509
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.15051 4.02031
## sample estimates:
## mean in group 0 mean in group 1
## 16.6732 15.7383cohen.d(mult.pred.lm3.bdi$BDI2.2 ~ mult.pred.lm3.bdi$match)##
## Cohen's d
##
## d estimate: 0.08280847 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.1909154 0.3565323#### Model 4 (Pre-tx BDI-II and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.bdi) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.bdi$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.bdi$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.bdi[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:32)]
X_train_cbt_data = rename(X_train_cbt_data, y = BDI2.2)
X_test_cbt = neo.depr.bdi[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:32)]
X_test_cbt = rename(X_test_cbt, y = BDI2.2)
X_train_med_data = neo.depr.bdi[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:32)]
X_train_med_data = rename(X_train_med_data, y = BDI2.2)
X_test_med = neo.depr.bdi[med_indexnums_test,]
X_test_med = X_test_med[,c(1:32)]
X_test_med = rename(X_test_med, y = BDI2.2)
bdi.cbt.train.model = regression.pai(data = X_train_cbt_data)
bdi.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(bdi.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(bdi.med.train.model, X_test_med)
cbtpred_medgrp = predict(bdi.cbt.lm4, X_test_med)
medpred_cbtgrp = predict(bdi.med.lm4, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm4.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm4.bdi = as.data.frame(mult.pred.lm4.bdi)
mult.pred.lm4.bdi$mean = rowMeans(cbind(mult.pred.lm4.bdi$V1, mult.pred.lm4.bdi$V2, mult.pred.lm4.bdi$V3, mult.pred.lm4.bdi$V4, mult.pred.lm4.bdi$V5,
mult.pred.lm4.bdi$V6, mult.pred.lm4.bdi$V7, mult.pred.lm4.bdi$V8, mult.pred.lm4.bdi$V9, mult.pred.lm4.bdi$V10))
describe(abs(mult.pred.lm4.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 7.37 6.05 6.04 6.59 5.56 0.14 34.2 34.07 1.34 2.15
## se Q0.25 Q0.75
## 1 0.42 2.45 10.79mult.pred.lm4.bdi = cbind(mult.pred.lm4.bdi, neo.depr.bdi)
mult.pred.lm4.bdi$indication = if_else(mult.pred.lm4.bdi$mean < 0, 0, 1)
cbt.indicated.lm4.bdi = filter(mult.pred.lm4.bdi, indication == 0)
med.indicated.lm4.bdi = filter(mult.pred.lm4.bdi, indication == 1)
# Analyses across tx group
mult.pred.lm4.bdi$match = if_else(mult.pred.lm4.bdi$indication==mult.pred.lm4.bdi$txgrp, 0, 1)
describeBy(mult.pred.lm4.bdi$BDI2.2, group = mult.pred.lm4.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 113 16.52 11.31 13.21 15.71 10.69 0 47 47 0.63 -0.27
## se Q0.25 Q0.75
## 1 1.06 8 25
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 95 15.87 11.28 14 14.85 11.86 0 51 51 0.81 0.14
## se Q0.25 Q0.75
## 1 1.16 7 22.5t.test(mult.pred.lm4.bdi$BDI2.2 ~ mult.pred.lm4.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm4.bdi$BDI2.2 by mult.pred.lm4.bdi$match
## t = 0.41199, df = 200.04, p-value = 0.6808
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.452115 3.747363
## sample estimates:
## mean in group 0 mean in group 1
## 16.51952 15.87189cohen.d(mult.pred.lm4.bdi$BDI2.2 ~ mult.pred.lm4.bdi$match)##
## Cohen's d
##
## d estimate: 0.05733719 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.2171526 0.3318270#### Model 5 (Every IV)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.bdi) # Sample size for BDI-II scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.bdi$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.bdi$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.bdi[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:32, 35, 36)]
X_train_cbt_data = rename(X_train_cbt_data, y = BDI2.2)
X_test_cbt = neo.depr.bdi[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:32, 35, 36)]
X_test_cbt = rename(X_test_cbt, y = BDI2.2)
X_train_med_data = neo.depr.bdi[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:32, 35, 36)]
X_train_med_data = rename(X_train_med_data, y = BDI2.2)
X_test_med = neo.depr.bdi[med_indexnums_test,]
X_test_med = X_test_med[,c(1:32, 35, 36)]
X_test_med = rename(X_test_med, y = BDI2.2)
bdi.cbt.train.model = regression.pai(data = X_train_cbt_data)
bdi.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(bdi.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(bdi.med.train.model, X_test_med)
cbtpred_medgrp = predict(bdi.cbt.lm5, X_test_med)
medpred_cbtgrp = predict(bdi.med.lm5, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted BDI-II score in CBT minus predicted BDI-II score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm5.bdi[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm5.bdi = as.data.frame(mult.pred.lm5.bdi)
mult.pred.lm5.bdi$mean = rowMeans(cbind(mult.pred.lm5.bdi$V1, mult.pred.lm5.bdi$V2, mult.pred.lm5.bdi$V3, mult.pred.lm5.bdi$V4, mult.pred.lm5.bdi$V5,
mult.pred.lm5.bdi$V6, mult.pred.lm5.bdi$V7, mult.pred.lm5.bdi$V8, mult.pred.lm5.bdi$V9, mult.pred.lm5.bdi$V10))
describe(abs(mult.pred.m1.bdi$mean), quant = c(.25, .75))## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 208 2.47 1.8 2.14 2.31 1.78 0.02 10.12 10.1 1.05 1.52
## se Q0.25 Q0.75
## 1 0.12 1.14 3.71mult.pred.lm5.bdi = cbind(mult.pred.lm5.bdi, neo.depr.bdi)
mult.pred.lm5.bdi$indication = if_else(mult.pred.lm5.bdi$mean < 0, 0, 1)
mult.pred.lm5.bdi = mult.pred.lm5.bdi[,c("indication", "mean", "txgrp", "BDI2.2")]
cbt.indicated.lm5.bdi = filter(mult.pred.lm5.bdi, indication == 0)
med.indicated.lm5.bdi = filter(mult.pred.lm5.bdi, indication == 1)
# Analyses across tx group
mult.pred.lm5.bdi$match = if_else(mult.pred.lm5.bdi$indication==mult.pred.lm5.bdi$txgrp, 0, 1)
describeBy(mult.pred.lm5.bdi$BDI2.2, group = mult.pred.lm5.bdi$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis
## 1 1 110 16.19 11.66 13 15.06 10.38 0 51 51 0.86 0.16
## se Q0.25 Q0.75
## 1 1.11 7.25 22
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 98 16.26 10.87 15 15.61 11.86 0 44 44 0.5 -0.51 1.1
## Q0.25 Q0.75
## 1 7.25 23t.test(mult.pred.lm5.bdi$BDI2.2 ~ mult.pred.lm5.bdi$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm5.bdi$BDI2.2 by mult.pred.lm5.bdi$match
## t = -0.048194, df = 205.57, p-value = 0.9616
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.157352 3.006675
## sample estimates:
## mean in group 0 mean in group 1
## 16.18823 16.26357cohen.d(mult.pred.lm5.bdi$BDI2.2 ~ mult.pred.lm5.bdi$match)##
## Cohen's d
##
## d estimate: -0.00666736 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.2805286 0.26719396.3 HAM-D
## Creating datasets, one for each tx group
iv.hd.process.cbt = subset(neo.depr.cbt.hd, select = iv.hd.names)
iv.hd.processed.cbt = standardizing(data = iv.hd.process.cbt, var.info = hd.info)
hd.cbt.z = iv.hd.processed.cbt
hd.cbt.z$y = neo.depr.cbt.hd$hamd2
hd.cbt.outcome = select(neo.depr.cbt.hd, hamd2)
iv.hd.process.med = subset(neo.depr.med.hd, select = iv.hd.names)
iv.hd.processed.med = standardizing(data = iv.hd.process.med, var.info = hd.info)
hd.med.z = iv.hd.processed.med
hd.med.z$y = neo.depr.med.hd$hamd2
hd.med.outcome = select(neo.depr.med.hd, hamd2)
## Creating datasets based on each submodel
# Model 1: Pre-tx HAM-D and demographics
iv.hd.processed.cbt1 = iv.hd.processed.cbt[,c(31:33)]
hd.cbt.z1 = iv.hd.processed.cbt1
hd.cbt.z1$y = neo.depr.cbt.hd$hamd2
iv.hd.reg.cbt1 = iv.hd.process.cbt[,c(31:33)]
hd.cbt.reg1 = iv.hd.reg.cbt1
hd.cbt.reg1$y = neo.depr.cbt.hd$hamd2
iv.hd.processed.med1 = iv.hd.processed.med[,c(31:33)]
hd.med.z1 = iv.hd.processed.med1
hd.med.z1$y = neo.depr.med.hd$hamd2
iv.hd.reg.med1 = iv.hd.process.med[,c(31:33)]
hd.med.reg1 = iv.hd.reg.med1
hd.med.reg1$y = neo.depr.med.hd$hamd2
# Model 2: FFM facets only
iv.hd.processed.cbt2 = iv.hd.processed.cbt[,c(1:30)]
hd.cbt.z2 = iv.hd.processed.cbt2
hd.cbt.z2$y = neo.depr.cbt.hd$hamd2
iv.hd.reg.cbt2 = iv.hd.process.cbt[,c(1:30)]
hd.cbt.reg2 = iv.hd.reg.cbt2
hd.cbt.reg2$y = neo.depr.cbt.hd$hamd2
iv.hd.processed.med2 = iv.hd.processed.med[,c(1:30)]
hd.med.z2 = iv.hd.processed.med2
hd.med.z2$y = neo.depr.med.hd$hamd2
iv.hd.reg.med2 = iv.hd.process.med[,c(1:30)]
hd.med.reg2 = iv.hd.reg.med2
hd.med.reg2$y = neo.depr.med.hd$hamd2
# Model 3: Demographics and FFM facets
iv.hd.processed.cbt3 = iv.hd.processed.cbt[,c(1:30, 32, 33)]
hd.cbt.z3 = iv.hd.processed.cbt3
hd.cbt.z3$y = neo.depr.cbt.hd$hamd2
iv.hd.reg.cbt3 = iv.hd.process.cbt[,c(1:30, 32, 33)]
hd.cbt.reg3 = iv.hd.reg.cbt3
hd.cbt.reg3$y = neo.depr.cbt.hd$hamd2
iv.hd.processed.med3 = iv.hd.processed.med[,c(1:30, 32, 33)]
hd.med.z3 = iv.hd.processed.med3
hd.med.z3$y = neo.depr.med.hd$hamd2
iv.hd.reg.med3 = iv.hd.process.med[,c(1:30, 32, 33)]
hd.med.reg3 = iv.hd.reg.med3
hd.med.reg3$y = neo.depr.med.hd$hamd2
# Model 4: Pre-tx HAM-D and FFM facets
iv.hd.processed.cbt4 = iv.hd.processed.cbt[,c(1:31)]
hd.cbt.z4 = iv.hd.processed.cbt4
hd.cbt.z4$y = neo.depr.cbt.hd$hamd2
iv.hd.reg.cbt4 = iv.hd.process.cbt[,c(1:31)]
hd.cbt.reg4 = iv.hd.reg.cbt4
hd.cbt.reg4$y = neo.depr.cbt.hd$hamd2
iv.hd.processed.med4 = iv.hd.processed.med[,c(1:31)]
hd.med.z4 = iv.hd.processed.med4
hd.med.z4$y = neo.depr.med.hd$hamd2
iv.hd.reg.med4 = iv.hd.process.med[,c(1:31)]
hd.med.reg4 = iv.hd.reg.med4
hd.med.reg4$y = neo.depr.med.hd$hamd2
# Model 5: Every IV
iv.hd.processed.cbt5 = iv.hd.processed.cbt
hd.cbt.z5 = iv.hd.processed.cbt5
hd.cbt.z5$y = neo.depr.cbt.hd$hamd2
iv.hd.reg.cbt5 = iv.hd.process.cbt
hd.cbt.reg5 = iv.hd.reg.cbt5
hd.cbt.reg5$y = neo.depr.cbt.hd$hamd2
iv.hd.processed.med5 = iv.hd.processed.med
hd.med.z5 = iv.hd.processed.med5
hd.med.z5$y = neo.depr.med.hd$hamd2
iv.hd.reg.med5 = iv.hd.process.med
hd.med.reg5 = iv.hd.reg.med5
hd.med.reg5$y = neo.depr.med.hd$hamd26.3.1 Elastic net regression models for counterfactual estimates
hd.cbt.model1 = elastic.net(data = hd.cbt.z1, zscored.ivs = iv.hd.processed.cbt1, outcome = hd.cbt.outcome)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## TrainRMSE TrainRsquared TrainMAE method
## 1 5.890046 0.06246622 4.808253 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.25 2 5.890046 0.06246622 4.808253 0.4479005 0.05713575 0.3465786
## alpha lambda
## 82 0.25 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3095238
## hamd1 0.5794066
## sex .
## age .hd.med.model1 = elastic.net(data = hd.med.z1, zscored.ivs = iv.hd.processed.med1, outcome = hd.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 7.282363 0.1281583 6.14113 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0 1 7.282363 0.1281583 6.14113 0.3307389 0.07929033 0.2928039
## alpha lambda
## 21 0 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.0168482
## hamd1 2.3505048
## sex 1.4420041
## age 0.4206978hd.cbt.model2 = elastic.net(data = hd.cbt.z2, zscored.ivs = iv.hd.processed.cbt2, outcome = hd.cbt.outcome)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## TrainRMSE TrainRsquared TrainMAE method
## 1 5.900213 0.2117159 4.845066 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 2 5.900213 0.2117159 4.845066 0.4593053 NA 0.320861
## alpha lambda
## 205 1 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.309524
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .hd.med.model2 = elastic.net(data = hd.med.z2, zscored.ivs = iv.hd.processed.med2, outcome = hd.med.outcome)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## TrainRMSE TrainRsquared TrainMAE method
## 1 7.367293 0.1092808 6.211867 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.25 1.1 7.367293 0.1092808 6.211867 0.4520457 0.07474903 0.3515339
## alpha lambda
## 64 0.25 1.1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.34439834
## NRN1.1 0.69411841
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.63968416
## NRE3.1 0.71112388
## NRE4.1 -0.43837790
## NRE5.1 .
## NRE6.1 -0.67108760
## NRO1.1 .
## NRO2.1 -0.27013400
## NRO3.1 .
## NRO4.1 -0.48365323
## NRO5.1 .
## NRO6.1 -0.07518779
## NRA1.1 -0.88342719
## NRA2.1 .
## NRA3.1 0.29543874
## NRA4.1 .
## NRA5.1 0.20249330
## NRA6.1 0.98110353
## NRC1.1 .
## NRC2.1 1.21610356
## NRC3.1 .
## NRC4.1 0.42556000
## NRC5.1 .
## NRC6.1 .hd.cbt.model3 = elastic.net(data = hd.cbt.z3, zscored.ivs = iv.hd.processed.cbt3, outcome = hd.cbt.outcome)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## TrainRMSE TrainRsquared TrainMAE method
## 1 5.900213 0.2117159 4.845066 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 2 5.900213 0.2117159 4.845066 0.4593053 NA 0.320861
## alpha lambda
## 205 1 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.309524
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .hd.med.model3 = elastic.net(data = hd.med.z3, zscored.ivs = iv.hd.processed.med3, outcome = hd.med.outcome)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## TrainRMSE TrainRsquared TrainMAE method
## 1 7.372079 0.112405 6.205735 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.25 1.05 7.372079 0.112405 6.205735 0.4909799 0.08073342 0.3794093
## alpha lambda
## 63 0.25 1.05
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.663429194
## NRN1.1 0.622508348
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.002677756
## NRE2.1 -0.639352942
## NRE3.1 0.781094801
## NRE4.1 -0.450863251
## NRE5.1 .
## NRE6.1 -0.654061972
## NRO1.1 .
## NRO2.1 -0.304196636
## NRO3.1 .
## NRO4.1 -0.634539955
## NRO5.1 .
## NRO6.1 -0.021436130
## NRA1.1 -0.992079244
## NRA2.1 .
## NRA3.1 0.290942476
## NRA4.1 .
## NRA5.1 0.162793176
## NRA6.1 1.003805904
## NRC1.1 .
## NRC2.1 1.203966848
## NRC3.1 .
## NRC4.1 0.373040452
## NRC5.1 .
## NRC6.1 .
## sex 1.041423044
## age 0.310344497hd.cbt.model4 = elastic.net(data = hd.cbt.z4, zscored.ivs = iv.hd.processed.cbt4, outcome = hd.cbt.outcome)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## TrainRMSE TrainRsquared TrainMAE method
## 1 5.900213 0.2117159 4.845066 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 2 5.900213 0.2117159 4.845066 0.4593053 NA 0.320861
## alpha lambda
## 205 1 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.309524
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .hd.med.model4 = elastic.net(data = hd.med.z4, zscored.ivs = iv.hd.processed.med4, outcome = hd.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 7.07094 0.1734059 5.897891 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.25 1.15 7.07094 0.1734059 5.897891 0.3277262 0.06743807 0.3053968
## alpha lambda
## 65 0.25 1.15
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.34439834
## NRN1.1 0.17659619
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.66565024
## NRE3.1 0.48374884
## NRE4.1 -0.39579186
## NRE5.1 .
## NRE6.1 -0.69168218
## NRO1.1 .
## NRO2.1 -0.06974789
## NRO3.1 .
## NRO4.1 -0.37345308
## NRO5.1 .
## NRO6.1 -0.06176392
## NRA1.1 -0.90828148
## NRA2.1 .
## NRA3.1 0.28535883
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.85205419
## NRC1.1 .
## NRC2.1 1.03911384
## NRC3.1 .
## NRC4.1 0.40724070
## NRC5.1 .
## NRC6.1 .
## hamd1 1.89644636hd.cbt.model5 = elastic.net(data = hd.cbt.z5, zscored.ivs = iv.hd.processed.cbt5, outcome = hd.cbt.outcome)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## TrainRMSE TrainRsquared TrainMAE method
## 1 5.900213 0.2117159 4.845066 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 2 5.900213 0.2117159 4.845066 0.4593053 NA 0.320861
## alpha lambda
## 205 1 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.309524
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .hd.med.model5 = elastic.net(data = hd.med.z5, zscored.ivs = iv.hd.processed.med5, outcome = hd.med.outcome)## TrainRMSE TrainRsquared TrainMAE method
## 1 7.076317 0.1744851 5.913451 glmnet
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 0.25 1.15 7.076317 0.1744851 5.913451 0.3639072 0.0769007 0.3325156
## alpha lambda
## 65 0.25 1.15
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.807575162
## NRN1.1 0.107845740
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.008065911
## NRE1.1 .
## NRE2.1 -0.656703309
## NRE3.1 0.521876375
## NRE4.1 -0.382527107
## NRE5.1 .
## NRE6.1 -0.673394229
## NRO1.1 .
## NRO2.1 -0.080956046
## NRO3.1 .
## NRO4.1 -0.503460545
## NRO5.1 .
## NRO6.1 -0.014062265
## NRA1.1 -0.969369000
## NRA2.1 .
## NRA3.1 0.262404791
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.842424302
## NRC1.1 .
## NRC2.1 1.008812797
## NRC3.1 .
## NRC4.1 0.356101487
## NRC5.1 .
## NRC6.1 .
## hamd1 1.888256189
## sex 0.952119244
## age 0.2300254786.3.2 Ordinary least squares models for counterfactual estimates
hd.cbt.lm1 = lm(y ~ ., data = hd.cbt.reg1)
hd.med.lm1 = lm(y ~ ., data = hd.med.reg1)
hd.cbt.lm2 = lm(y ~ ., data = hd.cbt.reg2)
hd.med.lm2 = lm(y ~ ., data = hd.med.reg2)
hd.cbt.lm3 = lm(y ~ ., data = hd.cbt.reg3)
hd.med.lm3 = lm(y ~ ., data = hd.med.reg3)
hd.cbt.lm4 = lm(y ~ ., data = hd.cbt.reg4)
hd.med.lm4 = lm(y ~ ., data = hd.med.reg4)
hd.cbt.lm5 = lm(y ~ ., data = hd.cbt.reg5)
hd.med.lm5 = lm(y ~ ., data = hd.med.reg5)
hd.cbt.lm6 = lm(y ~ hamd1, data = hd.cbt.reg5)
hd.med.lm6 = lm(y ~ hamd1, data = hd.med.reg5)6.3.3 Preparing data and matrices for PAIs
cv.num = 10 # Number of times to run full CV
hd.cbt.z$txgrp = neo.depr.cbt.hd$txgrp
hd.med.z$txgrp = neo.depr.med.hd$txgrp
hd.pai = rbind(hd.cbt.z, hd.med.z)
cv.tx = as.factor(hd.pai$txgrp)
cv.tx2 = as.factor(neo.depr.hd$txgrp)
##### Generating matrices to save results ####
mult.pred.m1.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.m2.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.m3.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.m4.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.m5.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.lm.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.lm1.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.lm2.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.lm3.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.lm4.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)
mult.pred.lm5.hd = matrix(NA, nrow = nrow(hd.pai), ncol = 10)6.3.4 PAI analyses (estimates and follow-ups)
#### Model 1 (Pre-tx HAM-D and demographics)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(hd.pai) # Sample size for HAM-D scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(hd.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(hd.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = hd.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(31:34)] # Taking out txgrp variable
X_train_cbt_enfunc = X_train_cbt_data[,c(1:3)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = hd.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(31:33)] # Taking out txgrp variable and y
X_train_med_data = hd.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(31:34)]
X_train_med_enfunc = X_train_med_data[,c(1:3)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = hd.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(31:33)]
hd.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
hd.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(hd.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(hd.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(hd.cbt.model1, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(hd.med.model1, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted HAM-D score in CBT minus predicted HAM-D score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m1.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.5872664
## hamd1 0.8158874
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.7917644
## hamd1 1.7586483
## sex 1.0416281
## age 0.4273731
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.691176
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 4.1921225
## hamd1 2.5423344
## sex 2.9491229
## age 0.6654941
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3754140
## hamd1 0.8833946
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.304896
## hamd1 2.051853
## sex .
## age .
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.447761
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3630453
## hamd1 2.7370433
## sex 1.2909021
## age 0.3403832
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.436137
## hamd1 0.315269
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.507774
## hamd1 2.733205
## sex 1.812713
## age .
## running cv # 2
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.1823626
## hamd1 0.8392395
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.818679
## hamd1 2.233453
## sex 1.773171
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.470624
## hamd1 1.019583
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.7223582
## hamd1 2.3274877
## sex 1.3468966
## age 0.5189942
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3500134
## hamd1 2.9502852
## sex 1.2040002
## age 0.3090907
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.426471
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.1297101
## hamd1 2.6176843
## sex 1.4003697
## age 0.8341449
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.2428223
## hamd1 0.4475446
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.416365
## hamd1 2.213019
## sex 1.268847
## age 0.284277
## running cv # 3
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.9691112
## hamd1 2.5471647
## sex 0.9574225
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.6087356
## hamd1 0.5363663
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.6803416
## hamd1 2.2627562
## sex 1.6090830
## age 0.5897479
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.934894
## hamd1 2.196019
## sex 2.137107
## age 0.586873
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.573529
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.8179184
## hamd1 1.9851907
## sex 1.4626398
## age 0.3375089
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.05207085
## hamd1 1.10909577
## sex -0.06026877
## age -0.16511611
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.3444736
## hamd1 2.5006019
## sex .
## age 0.1280766
## running cv # 4
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.367647
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.2140219
## hamd1 1.4175334
## sex 0.6281615
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.313433
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.3054120
## hamd1 2.6824942
## sex 0.9018479
## age 0.5525219
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.985075
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.9565490
## hamd1 2.4951605
## sex 1.9174977
## age 0.6901268
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.97533092
## hamd1 1.89876552
## sex 1.15413697
## age 0.02332646
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.2756512
## hamd1 2.5524983
## sex 1.7521301
## age 0.3369853
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.077788e+00
## hamd1 1.079775e+00
## sex -8.673231e-05
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.602998
## hamd1 1.838190
## sex .
## age .
## running cv # 5
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.97301397
## hamd1 0.08734381
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.6701713
## hamd1 2.0817188
## sex 1.8017005
## age 0.6214245
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.313433
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.2534379
## hamd1 2.6398164
## sex 1.4516248
## age 0.7197644
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.64923941
## hamd1 0.81216799
## sex 0.04708123
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.30770877
## hamd1 2.21938794
## sex 1.68612042
## age 0.06563148
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.0523319
## hamd1 0.8423268
## sex 0.1252757
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.244574
## hamd1 2.248652
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.2446328
## hamd1 0.5130128
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.8449905
## hamd1 2.0475610
## sex 1.5320919
## age 0.2759178
## running cv # 6
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.1383999
## hamd1 0.7405779
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.477195
## hamd1 1.792466
## sex .
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.764706
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.542258
## hamd1 2.434465
## sex 1.561218
## age 0.352523
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.880597
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.0136336
## hamd1 1.6483806
## sex 1.3534851
## age 0.2526217
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.8945093
## hamd1 1.1643310
## sex 0.7783343
## age -0.2398619
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.5794608
## hamd1 2.3927151
## sex 1.3911435
## age 0.4387522
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## hamd1 0.00000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.0899493
## hamd1 3.1534054
## sex 1.8917854
## age 0.6188929
## running cv # 7
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.179104
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.281101
## hamd1 2.250108
## sex .
## age .
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3589859
## hamd1 0.3907658
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.171475
## hamd1 2.270874
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.199252
## hamd1 1.048503
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.1271657
## hamd1 2.4703809
## sex 2.1032073
## age 0.3837977
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.089552
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.7978916
## hamd1 1.9222685
## sex 1.6126037
## age 0.4741678
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## hamd1 0.00000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.7636637
## hamd1 2.7636989
## sex 2.2287060
## age 0.6224722
## running cv # 8
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.1394952
## hamd1 0.6511166
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.1884776
## hamd1 2.1952276
## sex 1.4586791
## age 0.5040965
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.104478
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.8485329
## hamd1 2.4077855
## sex 1.2211167
## age 0.1569876
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.074627
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.6138070
## hamd1 2.5937179
## sex 1.5379969
## age 0.4813463
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.985075
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.2941254
## hamd1 2.4841928
## sex 1.3250781
## age 0.4778254
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.223881
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.81778341
## hamd1 1.92761024
## sex 0.01416673
## age .
## running cv # 9
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.85861424
## hamd1 0.08290911
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.7251266
## hamd1 1.8241967
## sex 1.5396569
## age 0.4449853
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.265318
## hamd1 1.263417
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3331904
## hamd1 2.4908758
## sex 1.1243664
## age 0.4327867
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.264706
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.5513877
## hamd1 2.3609932
## sex 1.3187311
## age 0.6354285
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.029851
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.9500872
## hamd1 3.0859660
## sex 1.5711609
## age 0.4992224
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.1602478
## hamd1 0.3899372
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.206501
## hamd1 2.076143
## sex 1.193793
## age .
## running cv # 10
## running fold # 1
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.552239
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3226475
## hamd1 2.1511304
## sex 1.3282545
## age 0.3183999
## running fold # 2
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.073529
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.638768
## hamd1 2.385584
## sex .
## age .
## running fold # 3
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.6758336
## hamd1 0.6230053
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.2013509
## hamd1 2.1950748
## sex 2.0182458
## age 0.3216369
## running fold # 4
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.850746
## hamd1 0.000000
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.785439
## hamd1 1.420753
## sex .
## age .
## running fold # 5
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3968546
## hamd1 0.5890166
## sex .
## age .
## 4 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.8226063
## hamd1 2.4332193
## sex 1.5555282
## age 0.5394858mult.pred.m1.hd = as.data.frame(mult.pred.m1.hd)
mult.pred.m1.hd$mean = rowMeans(cbind(mult.pred.m1.hd$V1, mult.pred.m1.hd$V2, mult.pred.m1.hd$V3, mult.pred.m1.hd$V4, mult.pred.m1.hd$V5,
mult.pred.m1.hd$V6, mult.pred.m1.hd$V7, mult.pred.m1.hd$V8, mult.pred.m1.hd$V9, mult.pred.m1.hd$V10))
mult.pred.m1.hd = cbind(mult.pred.m1.hd, hd.pai)
mult.pred.m1.hd$indication = if_else(mult.pred.m1.hd$mean < 0, 0, 1)
cbt.indicated.m1.hd = filter(mult.pred.m1.hd, indication == 0)
med.indicated.m1.hd = filter(mult.pred.m1.hd, indication == 1)
# Analyses across tx group
mult.pred.m1.hd$match = if_else(mult.pred.m1.hd$indication==mult.pred.m1.hd$txgrp, 0, 1)
describeBy(mult.pred.m1.hd$y, group = mult.pred.m1.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 99 7.53 6.15 6 6.95 5.93 0 26 26 0.79 -0.09 0.62
## Q0.25 Q0.75
## 1 3 11.5
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 226 9.38 7.78 7 8.72 8.9 0 33 33 0.63 -0.57 0.52
## Q0.25 Q0.75
## 1 3 16t.test(mult.pred.m1.hd$y ~ mult.pred.m1.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m1.hd$y by mult.pred.m1.hd$match
## t = -2.3059, df = 233.5, p-value = 0.02199
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.4486240 -0.2707824
## sample estimates:
## mean in group 0 mean in group 1
## 7.525253 9.384956cohen.d(mult.pred.m1.hd$y ~ mult.pred.m1.hd$match)##
## Cohen's d
##
## d estimate: -0.2538446 (small)
## 95 percent confidence interval:
## lower upper
## -0.49176152 -0.01592777#### Model 2 (FFM facets only)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(hd.pai) # Sample size for HAM-D scores
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(hd.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(hd.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = hd.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:30)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = hd.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30)]
X_train_med_data = hd.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 34)]
X_train_med_enfunc = X_train_med_data[,c(1:30)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = hd.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30)]
hd.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
hd.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(hd.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(hd.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(hd.cbt.model2, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(hd.med.model2, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
# Predicted HAM-D score in CBT minus predicted HAM-D score in med - negative means CBT indicated, positive means meds
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m2.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.656716
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.40995048
## NRN1.1 0.37407411
## NRN2.1 0.37934979
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.74640373
## NRE3.1 0.63666740
## NRE4.1 -0.56196931
## NRE5.1 .
## NRE6.1 -1.02592225
## NRO1.1 -0.41263557
## NRO2.1 -0.07762960
## NRO3.1 .
## NRO4.1 -0.22372772
## NRO5.1 .
## NRO6.1 -0.01918772
## NRA1.1 -0.97297730
## NRA2.1 .
## NRA3.1 0.12401990
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.92723362
## NRC1.1 .
## NRC2.1 0.83301909
## NRC3.1 .
## NRC4.1 0.59449420
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.691176
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.160213911
## NRN1.1 0.732620982
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.453682905
## NRE3.1 .
## NRE4.1 .
## NRE5.1 -0.234351867
## NRE6.1 -0.217136663
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.120842400
## NRO5.1 .
## NRO6.1 -0.282579095
## NRA1.1 -0.760970443
## NRA2.1 .
## NRA3.1 0.457203626
## NRA4.1 .
## NRA5.1 0.005597324
## NRA6.1 0.497035918
## NRC1.1 .
## NRC2.1 0.720142011
## NRC3.1 .
## NRC4.1 0.350801307
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.343284
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.35097460
## NRN1.1 0.73720222
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.26290673
## NRE2.1 -0.73479707
## NRE3.1 0.61505586
## NRE4.1 -0.97683917
## NRE5.1 .
## NRE6.1 -0.08080795
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.62016635
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.10871811
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.98805846
## NRC1.1 .
## NRC2.1 1.41294742
## NRC3.1 .
## NRC4.1 0.01406536
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.447761
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.5548946
## NRN1.1 0.9511381
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.6875925
## NRE3.1 1.0035495
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.3895182
## NRO1.1 .
## NRO2.1 -0.6365057
## NRO3.1 .
## NRO4.1 -0.8121836
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.6701109
## NRA2.1 .
## NRA3.1 0.5855166
## NRA4.1 .
## NRA5.1 0.3015823
## NRA6.1 1.0697664
## NRC1.1 .
## NRC2.1 1.0048386
## NRC3.1 .
## NRC4.1 0.2644043
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.41791
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.323918070
## NRN1.1 0.620259164
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.149471015
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.395080561
## NRE3.1 0.868599280
## NRE4.1 -0.606574457
## NRE5.1 .
## NRE6.1 -1.061508015
## NRO1.1 .
## NRO2.1 -0.514274977
## NRO3.1 -0.186571493
## NRO4.1 -0.476018038
## NRO5.1 .
## NRO6.1 -0.002226406
## NRA1.1 -1.378683774
## NRA2.1 .
## NRA3.1 0.062574959
## NRA4.1 0.585047867
## NRA5.1 0.412722356
## NRA6.1 1.209354547
## NRC1.1 .
## NRC2.1 1.908493583
## NRC3.1 .
## NRC4.1 0.681277008
## NRC5.1 -0.048544898
## NRC6.1 -0.224247000
## running cv # 2
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.2379840
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.1217908
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.64958094
## NRN1.1 0.91937828
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.36533872
## NRE3.1 0.42672705
## NRE4.1 -0.50769369
## NRE5.1 .
## NRE6.1 -0.57853511
## NRO1.1 .
## NRO2.1 -0.12362620
## NRO3.1 .
## NRO4.1 -0.20353380
## NRO5.1 .
## NRO6.1 -0.19430365
## NRA1.1 -1.02002334
## NRA2.1 .
## NRA3.1 0.65062177
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.02032001
## NRC1.1 .
## NRC2.1 1.50358086
## NRC3.1 .
## NRC4.1 0.00601786
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.462687
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.8512267
## NRN1.1 0.3806625
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.6559414
## NRE3.1 0.1417207
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.2021034
## NRO1.1 .
## NRO2.1 -0.1941390
## NRO3.1 .
## NRO4.1 -0.2162388
## NRO5.1 .
## NRO6.1 -0.1561279
## NRA1.1 -0.2672915
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.7338330
## NRA6.1 0.7902619
## NRC1.1 .
## NRC2.1 0.6779276
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.45523426
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.01157089
## NRE3.1 .
## NRE4.1 -0.01197122
## NRE5.1 .
## NRE6.1 -0.86545882
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.29963779
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.59990997
## NRA2.1 0.22606750
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.12940347
## NRC1.1 .
## NRC2.1 1.16122569
## NRC3.1 .
## NRC4.1 0.74903394
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.426471
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.37849828
## NRN1.1 0.69250391
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.05778752
## NRN6.1 .
## NRE1.1 -0.18851644
## NRE2.1 -0.89914638
## NRE3.1 0.83526937
## NRE4.1 -0.21222506
## NRE5.1 .
## NRE6.1 -0.65367063
## NRO1.1 .
## NRO2.1 -0.05178226
## NRO3.1 .
## NRO4.1 -0.62204550
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.57221940
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.78732341
## NRC1.1 .
## NRC2.1 1.12515781
## NRC3.1 .
## NRC4.1 0.23284298
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.3966415
## NRN1.1 0.7521549
## NRN2.1 0.2892924
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.2064919
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.5195675
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.4293526
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.5627686
## NRA2.1 .
## NRA3.1 0.5728576
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.6354821
## NRC1.1 .
## NRC2.1 0.6872616
## NRC3.1 .
## NRC4.1 0.0404250
## NRC5.1 .
## NRC6.1 .
## running cv # 3
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.60540120
## NRN1.1 0.87146346
## NRN2.1 .
## NRN3.1 0.18471319
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.24792884
## NRE2.1 -0.71113520
## NRE3.1 0.53437265
## NRE4.1 -0.44795104
## NRE5.1 0.00581957
## NRE6.1 -0.74316506
## NRO1.1 -0.03618397
## NRO2.1 -0.13472631
## NRO3.1 .
## NRO4.1 -0.06052261
## NRO5.1 .
## NRO6.1 -0.53093068
## NRA1.1 -0.38051408
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.51971303
## NRC1.1 .
## NRC2.1 1.21960000
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.671642
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.24599636
## NRN1.1 0.75000603
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.10402839
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -1.03544482
## NRE3.1 1.29643295
## NRE4.1 -0.45171181
## NRE5.1 .
## NRE6.1 -0.59475082
## NRO1.1 -0.28217335
## NRO2.1 -0.05147467
## NRO3.1 -0.03161332
## NRO4.1 -0.35114803
## NRO5.1 .
## NRO6.1 -0.07410037
## NRA1.1 -0.90844503
## NRA2.1 .
## NRA3.1 0.49824895
## NRA4.1 .
## NRA5.1 0.36094507
## NRA6.1 1.32136420
## NRC1.1 .
## NRC2.1 1.00687107
## NRC3.1 .
## NRC4.1 0.73723074
## NRC5.1 .
## NRC6.1 -0.25738580
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.29744519
## NRN1.1 0.74103951
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.26330789
## NRE3.1 0.64538536
## NRE4.1 -0.66108494
## NRE5.1 -0.10637874
## NRE6.1 -0.48659663
## NRO1.1 .
## NRO2.1 -0.31770661
## NRO3.1 .
## NRO4.1 -0.77606267
## NRO5.1 .
## NRO6.1 -0.01034932
## NRA1.1 -0.53485652
## NRA2.1 .
## NRA3.1 0.16969986
## NRA4.1 0.02088975
## NRA5.1 0.28673700
## NRA6.1 1.09840869
## NRC1.1 .
## NRC2.1 1.00484604
## NRC3.1 .
## NRC4.1 0.58133875
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.573529
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.14298174
## NRN1.1 0.22531711
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.17322065
## NRE3.1 .
## NRE4.1 -0.18464683
## NRE5.1 .
## NRE6.1 -0.65326875
## NRO1.1 .
## NRO2.1 -0.08741082
## NRO3.1 .
## NRO4.1 -0.29825426
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.01486162
## NRA2.1 .
## NRA3.1 0.28565129
## NRA4.1 .
## NRA5.1 0.43612813
## NRA6.1 0.72202505
## NRC1.1 .
## NRC2.1 1.33596602
## NRC3.1 .
## NRC4.1 0.66398393
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.90721467
## NRN1.1 .
## NRN2.1 0.18780804
## NRN3.1 0.49074326
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.95746226
## NRE2.1 0.43189785
## NRE3.1 -0.39526601
## NRE4.1 .
## NRE5.1 -0.02426094
## NRE6.1 -0.53014977
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.18879172
## NRO5.1 -0.12259889
## NRO6.1 -0.70609617
## NRA1.1 0.27440128
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.53164626
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.09551417
## NRC2.1 -0.64195187
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.5170707
## NRN1.1 0.8175907
## NRN2.1 0.1460828
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.7378480
## NRE3.1 0.5814044
## NRE4.1 -0.2938632
## NRE5.1 .
## NRE6.1 -0.5322689
## NRO1.1 .
## NRO2.1 -0.2491775
## NRO3.1 -0.1149109
## NRO4.1 -0.4689941
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.9942714
## NRA2.1 .
## NRA3.1 0.2672783
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.8580567
## NRC1.1 .
## NRC2.1 1.1187597
## NRC3.1 .
## NRC4.1 0.4741683
## NRC5.1 .
## NRC6.1 .
## running cv # 4
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.367647
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.15301216
## NRN1.1 0.19727596
## NRN2.1 0.20808309
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.08050119
## NRE3.1 0.08937387
## NRE4.1 -0.40297249
## NRE5.1 .
## NRE6.1 -0.47884521
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.27938421
## NRO5.1 .
## NRO6.1 -0.19885743
## NRA1.1 -0.04376438
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.24625507
## NRA6.1 0.31890567
## NRC1.1 .
## NRC2.1 0.99059178
## NRC3.1 .
## NRC4.1 0.41327497
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.2529098
## NRN1.1 0.2821457
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.3407410
## NRN5.1 .
## NRN6.1 .
## NRE1.1 1.1072444
## NRE2.1 0.2052317
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.3257535
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.4647373
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.3845158
## NRC2.1 -0.3571070
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.5687893
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.6992441
## NRN1.1 0.5086162
## NRN2.1 0.2319324
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.3101960
## NRE2.1 -0.6397802
## NRE3.1 0.4863041
## NRE4.1 -0.4381850
## NRE5.1 .
## NRE6.1 -0.5515311
## NRO1.1 -0.1042253
## NRO2.1 -0.3511067
## NRO3.1 -0.1833966
## NRO4.1 -0.2214678
## NRO5.1 .
## NRO6.1 -0.1226898
## NRA1.1 -1.0281301
## NRA2.1 .
## NRA3.1 0.6404480
## NRA4.1 .
## NRA5.1 0.1690449
## NRA6.1 1.1131970
## NRC1.1 .
## NRC2.1 0.9724788
## NRC3.1 .
## NRC4.1 0.4033797
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.21998763
## NRN1.1 0.69544842
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.59631878
## NRE3.1 0.22139181
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.44991548
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.37283760
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.38521110
## NRA2.1 .
## NRA3.1 0.04554407
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.92239651
## NRC1.1 .
## NRC2.1 1.01361230
## NRC3.1 .
## NRC4.1 0.49516832
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.761194
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.12052813
## NRN1.1 0.79234235
## NRN2.1 0.04889026
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.54955742
## NRE3.1 0.86646249
## NRE4.1 -0.09349753
## NRE5.1 .
## NRE6.1 -0.83734419
## NRO1.1 .
## NRO2.1 -0.43396181
## NRO3.1 .
## NRO4.1 -0.41370594
## NRO5.1 .
## NRO6.1 -0.02211696
## NRA1.1 -0.96591170
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.16874033
## NRA6.1 1.23994629
## NRC1.1 .
## NRC2.1 1.38213681
## NRC3.1 .
## NRC4.1 0.16899293
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.46472748
## NRN1.1 0.85838465
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.11674382
## NRE2.1 -0.73341074
## NRE3.1 0.47186419
## NRE4.1 -0.28372431
## NRE5.1 .
## NRE6.1 -0.51930060
## NRO1.1 .
## NRO2.1 -0.07873467
## NRO3.1 .
## NRO4.1 -0.69387028
## NRO5.1 0.07653071
## NRO6.1 .
## NRA1.1 -0.87633055
## NRA2.1 .
## NRA3.1 0.94731207
## NRA4.1 .
## NRA5.1 0.06309984
## NRA6.1 0.25969041
## NRC1.1 .
## NRC2.1 0.96934229
## NRC3.1 .
## NRC4.1 0.06712108
## NRC5.1 .
## NRC6.1 .
## running cv # 5
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.970149
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.778270889
## NRN1.1 1.049665819
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.129272535
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.446348269
## NRE2.1 -0.890591489
## NRE3.1 0.899985875
## NRE4.1 -0.718957452
## NRE5.1 0.162187870
## NRE6.1 -0.226492272
## NRO1.1 -0.109059332
## NRO2.1 -0.524842057
## NRO3.1 .
## NRO4.1 -0.198354983
## NRO5.1 .
## NRO6.1 -0.267436110
## NRA1.1 -0.731880517
## NRA2.1 0.020917393
## NRA3.1 0.341365916
## NRA4.1 .
## NRA5.1 0.024351198
## NRA6.1 1.196592193
## NRC1.1 .
## NRC2.1 1.518719073
## NRC3.1 .
## NRC4.1 0.004673663
## NRC5.1 .
## NRC6.1 -0.261619875
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.313433
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.52916414
## NRN1.1 0.96456834
## NRN2.1 0.24849782
## NRN3.1 -0.48330804
## NRN4.1 -0.32020168
## NRN5.1 0.32016566
## NRN6.1 -0.11037909
## NRE1.1 -0.21263029
## NRE2.1 -0.99884957
## NRE3.1 0.91445520
## NRE4.1 -0.79662679
## NRE5.1 -0.07135179
## NRE6.1 -0.75933246
## NRO1.1 0.18536130
## NRO2.1 -0.60511232
## NRO3.1 -0.36097399
## NRO4.1 -0.52182421
## NRO5.1 0.31481399
## NRO6.1 -0.38697215
## NRA1.1 -1.03706282
## NRA2.1 -0.42239133
## NRA3.1 1.07710994
## NRA4.1 0.63063023
## NRA5.1 0.12731601
## NRA6.1 1.51706768
## NRC1.1 -0.17249528
## NRC2.1 1.19198251
## NRC3.1 -0.06062627
## NRC4.1 0.82108922
## NRC5.1 -0.12378285
## NRC6.1 -0.02980331
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.83264529
## NRN1.1 0.24657983
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.08069059
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.10840052
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.59892180
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.01851043
## NRN1.1 0.73965748
## NRN2.1 0.46772762
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.26986574
## NRE3.1 0.34671476
## NRE4.1 -0.32111125
## NRE5.1 .
## NRE6.1 -0.51331346
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.56019093
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.51170850
## NRA2.1 .
## NRA3.1 0.09611921
## NRA4.1 .
## NRA5.1 0.35938215
## NRA6.1 0.65149551
## NRC1.1 -0.02799128
## NRC2.1 0.79273424
## NRC3.1 .
## NRC4.1 0.73889590
## NRC5.1 -0.09072936
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.00476324
## NRN1.1 0.45916817
## NRN2.1 .
## NRN3.1 0.16879155
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.33642409
## NRE2.1 -0.20099558
## NRE3.1 0.99774585
## NRE4.1 -0.24207788
## NRE5.1 .
## NRE6.1 -1.00687129
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.41861425
## NRO5.1 .
## NRO6.1 -0.02188202
## NRA1.1 -1.09276327
## NRA2.1 .
## NRA3.1 0.29660402
## NRA4.1 0.01004571
## NRA5.1 0.24107247
## NRA6.1 1.03248910
## NRC1.1 .
## NRC2.1 1.30174911
## NRC3.1 .
## NRC4.1 0.15922553
## NRC5.1 .
## NRC6.1 -0.07504393
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.36401473
## NRN1.1 0.30863457
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.45687758
## NRE3.1 .
## NRE4.1 .
## NRE5.1 -0.01006003
## NRE6.1 -0.59726902
## NRO1.1 -0.21850741
## NRO2.1 -0.03471135
## NRO3.1 .
## NRO4.1 -0.31559375
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.60591550
## NRA2.1 .
## NRA3.1 0.16451803
## NRA4.1 .
## NRA5.1 0.12912945
## NRA6.1 0.27942937
## NRC1.1 .
## NRC2.1 1.10719194
## NRC3.1 .
## NRC4.1 0.50542263
## NRC5.1 .
## NRC6.1 .
## running cv # 6
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.35364515
## NRN1.1 1.04136361
## NRN2.1 0.21467160
## NRN3.1 -0.20833677
## NRN4.1 -0.57215053
## NRN5.1 -0.07571638
## NRN6.1 -0.42808740
## NRE1.1 -0.74777547
## NRE2.1 -1.11303088
## NRE3.1 1.11918446
## NRE4.1 -0.87761388
## NRE5.1 0.17061695
## NRE6.1 -0.80142310
## NRO1.1 0.00868871
## NRO2.1 -0.44440089
## NRO3.1 0.01504657
## NRO4.1 -0.12911819
## NRO5.1 0.34975570
## NRO6.1 -0.61118520
## NRA1.1 -0.83917034
## NRA2.1 0.18836988
## NRA3.1 0.76028636
## NRA4.1 0.45080954
## NRA5.1 0.37708610
## NRA6.1 1.13950688
## NRC1.1 -0.39456151
## NRC2.1 1.18850730
## NRC3.1 -0.26596272
## NRC4.1 0.80898046
## NRC5.1 0.02195700
## NRC6.1 -0.32156976
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.764706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.24439637
## NRN1.1 0.64177082
## NRN2.1 0.02949208
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.31155139
## NRN6.1 .
## NRE1.1 -0.05855557
## NRE2.1 -1.17142931
## NRE3.1 0.92056439
## NRE4.1 -0.32863387
## NRE5.1 0.33530665
## NRE6.1 -0.81309223
## NRO1.1 -0.15119838
## NRO2.1 -0.26490314
## NRO3.1 .
## NRO4.1 -0.28574999
## NRO5.1 .
## NRO6.1 -0.03229063
## NRA1.1 -1.28043060
## NRA2.1 .
## NRA3.1 0.52104762
## NRA4.1 .
## NRA5.1 0.11229612
## NRA6.1 0.82894517
## NRC1.1 .
## NRC2.1 1.27172934
## NRC3.1 .
## NRC4.1 0.17455267
## NRC5.1 .
## NRC6.1 -0.07927077
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.880597
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.17449942
## NRN1.1 0.19711413
## NRN2.1 0.19205292
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.35752211
## NRE4.1 -0.51875421
## NRE5.1 .
## NRE6.1 -0.36448524
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -1.30188330
## NRO5.1 .
## NRO6.1 -0.04664667
## NRA1.1 -0.68405343
## NRA2.1 .
## NRA3.1 0.36578336
## NRA4.1 .
## NRA5.1 0.10682124
## NRA6.1 1.06774539
## NRC1.1 .
## NRC2.1 0.93159193
## NRC3.1 .
## NRC4.1 0.38601986
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.82209847
## NRN1.1 1.06571241
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.17019768
## NRE3.1 0.73461395
## NRE4.1 -0.07499826
## NRE5.1 .
## NRE6.1 -0.86256849
## NRO1.1 .
## NRO2.1 -0.14119242
## NRO3.1 .
## NRO4.1 -0.48559669
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.56443273
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.15769612
## NRA6.1 0.96461018
## NRC1.1 .
## NRC2.1 1.47002776
## NRC3.1 .
## NRC4.1 0.21976236
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.15882652
## NRN1.1 0.68015787
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.37337275
## NRE3.1 .
## NRE4.1 .
## NRE5.1 -0.27294710
## NRE6.1 -0.42630642
## NRO1.1 .
## NRO2.1 -0.17116282
## NRO3.1 .
## NRO4.1 -0.34580706
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.59799562
## NRA2.1 0.04593295
## NRA3.1 0.09456799
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.76477332
## NRC1.1 .
## NRC2.1 1.22696171
## NRC3.1 .
## NRC4.1 0.37598493
## NRC5.1 .
## NRC6.1 .
## running cv # 7
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.179104
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.22703070
## NRN1.1 1.11536216
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.05303947
## NRE2.1 -0.99006717
## NRE3.1 0.62594470
## NRE4.1 -0.29906789
## NRE5.1 .
## NRE6.1 -0.96839167
## NRO1.1 -0.01774656
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.21243097
## NRO5.1 .
## NRO6.1 -0.25187561
## NRA1.1 -0.64123597
## NRA2.1 .
## NRA3.1 0.19220376
## NRA4.1 .
## NRA5.1 0.02435888
## NRA6.1 0.92902852
## NRC1.1 .
## NRC2.1 0.61683689
## NRC3.1 .
## NRC4.1 0.45296288
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.3935673
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.1151960
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.1365565
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.7666791
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.4133881
## NRC1.1 -0.2333485
## NRC2.1 -1.3589840
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.3767023
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.19885515
## NRN1.1 0.50620448
## NRN2.1 0.37780807
## NRN3.1 0.27416815
## NRN4.1 -0.08591424
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.59347121
## NRE3.1 1.12449644
## NRE4.1 -0.98140074
## NRE5.1 .
## NRE6.1 -0.47248645
## NRO1.1 -0.18434182
## NRO2.1 -0.73372963
## NRO3.1 -0.40218392
## NRO4.1 -0.24859111
## NRO5.1 .
## NRO6.1 -0.45538851
## NRA1.1 -1.00779553
## NRA2.1 -0.13844128
## NRA3.1 0.76092948
## NRA4.1 .
## NRA5.1 0.57700375
## NRA6.1 1.78155492
## NRC1.1 -0.75480595
## NRC2.1 1.40162582
## NRC3.1 -0.10647963
## NRC4.1 0.86603728
## NRC5.1 .
## NRC6.1 -0.10390299
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.279412
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.45009627
## NRN1.1 0.72925862
## NRN2.1 0.05575595
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.28650360
## NRE3.1 .
## NRE4.1 -0.08905054
## NRE5.1 .
## NRE6.1 -0.45910264
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.31520574
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.56058158
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.00465456
## NRA6.1 0.72990048
## NRC1.1 .
## NRC2.1 1.09692208
## NRC3.1 .
## NRC4.1 0.39051304
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.089552
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.41273831
## NRN1.1 .
## NRN2.1 0.16911715
## NRN3.1 .
## NRN4.1 -0.19579178
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.41623998
## NRE3.1 0.92117154
## NRE4.1 -0.17409704
## NRE5.1 -0.02598221
## NRE6.1 -1.05569450
## NRO1.1 .
## NRO2.1 -0.58306099
## NRO3.1 .
## NRO4.1 -0.70576407
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.00744753
## NRA2.1 .
## NRA3.1 0.60534895
## NRA4.1 0.24153298
## NRA5.1 .
## NRA6.1 0.80782324
## NRC1.1 .
## NRC2.1 1.49244843
## NRC3.1 .
## NRC4.1 0.38004886
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.490246517
## NRN1.1 1.145544514
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.003408297
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.936825072
## NRE3.1 0.763378606
## NRE4.1 -0.644647501
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 -0.096952093
## NRO3.1 .
## NRO4.1 -0.608671310
## NRO5.1 0.151889555
## NRO6.1 .
## NRA1.1 -0.648976194
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.051702110
## NRA6.1 0.855687029
## NRC1.1 .
## NRC2.1 1.697830104
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 8
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.161765
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.28745531
## NRN1.1 0.52818363
## NRN2.1 0.68087869
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.09435858
## NRE2.1 -0.68498233
## NRE3.1 0.88408513
## NRE4.1 -0.48584460
## NRE5.1 .
## NRE6.1 -0.67628408
## NRO1.1 .
## NRO2.1 -0.28121027
## NRO3.1 -0.10072983
## NRO4.1 -0.73981761
## NRO5.1 .
## NRO6.1 -0.25548658
## NRA1.1 -0.97699740
## NRA2.1 .
## NRA3.1 0.73913082
## NRA4.1 0.23435666
## NRA5.1 .
## NRA6.1 1.38615774
## NRC1.1 .
## NRC2.1 1.10403164
## NRC3.1 .
## NRC4.1 0.30813092
## NRC5.1 .
## NRC6.1 -0.01519864
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.104478
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.93351561
## NRN1.1 0.55145157
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.64518932
## NRE3.1 0.24584693
## NRE4.1 -0.30860983
## NRE5.1 -0.03323816
## NRE6.1 -0.48323854
## NRO1.1 .
## NRO2.1 -0.55473947
## NRO3.1 .
## NRO4.1 -0.29179650
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.93748243
## NRA2.1 .
## NRA3.1 0.14772968
## NRA4.1 .
## NRA5.1 0.31459860
## NRA6.1 0.70113023
## NRC1.1 .
## NRC2.1 1.13234114
## NRC3.1 .
## NRC4.1 0.62958207
## NRC5.1 .
## NRC6.1 .
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.074627
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.29681417
## NRN1.1 0.42821458
## NRN2.1 0.38061849
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.49726646
## NRE3.1 1.09479001
## NRE4.1 -0.76936982
## NRE5.1 0.01548836
## NRE6.1 -0.71502404
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.53277764
## NRO5.1 .
## NRO6.1 -0.43050297
## NRA1.1 -0.50788642
## NRA2.1 .
## NRA3.1 0.21509098
## NRA4.1 .
## NRA5.1 0.68066538
## NRA6.1 0.84282929
## NRC1.1 .
## NRC2.1 1.45617233
## NRC3.1 .
## NRC4.1 0.75418428
## NRC5.1 .
## NRC6.1 -0.29173901
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.32020484
## NRN1.1 0.54128602
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.33995080
## NRE3.1 0.33996562
## NRE4.1 -0.68968366
## NRE5.1 .
## NRE6.1 -0.45497245
## NRO1.1 -0.15074996
## NRO2.1 -0.05928920
## NRO3.1 .
## NRO4.1 -0.08192243
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.84013945
## NRA2.1 .
## NRA3.1 0.04229594
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.55306691
## NRC1.1 .
## NRC2.1 1.11436235
## NRC3.1 .
## NRC4.1 0.10969779
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.223881
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.95119612
## NRN1.1 1.03054976
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.23780552
## NRE2.1 -0.67513454
## NRE3.1 0.67278843
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.70239000
## NRO1.1 .
## NRO2.1 -0.06322320
## NRO3.1 -0.18289313
## NRO4.1 -0.57143520
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.47773410
## NRA2.1 .
## NRA3.1 0.51246120
## NRA4.1 .
## NRA5.1 0.09513349
## NRA6.1 1.22663855
## NRC1.1 .
## NRC2.1 0.97387473
## NRC3.1 .
## NRC4.1 0.31183739
## NRC5.1 .
## NRC6.1 .
## running cv # 9
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.865672
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.02461901
## NRN1.1 0.18643670
## NRN2.1 0.12397001
## NRN3.1 0.03105063
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.01330890
## NRE2.1 -0.34096657
## NRE3.1 0.73362750
## NRE4.1 -0.76221663
## NRE5.1 -0.12532367
## NRE6.1 -0.98282595
## NRO1.1 .
## NRO2.1 -0.55455512
## NRO3.1 .
## NRO4.1 -0.30998226
## NRO5.1 0.29886451
## NRO6.1 -0.21797793
## NRA1.1 -0.91877451
## NRA2.1 0.09039453
## NRA3.1 0.68164330
## NRA4.1 .
## NRA5.1 0.14213159
## NRA6.1 0.62622030
## NRC1.1 .
## NRC2.1 1.36768286
## NRC3.1 .
## NRC4.1 0.53599813
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.223881
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.141249288
## NRN1.1 0.676476572
## NRN2.1 .
## NRN3.1 -0.005761245
## NRN4.1 .
## NRN5.1 0.051233908
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.756517265
## NRE3.1 0.779078020
## NRE4.1 -0.267556616
## NRE5.1 .
## NRE6.1 -0.561684940
## NRO1.1 -0.163414169
## NRO2.1 -0.312751239
## NRO3.1 .
## NRO4.1 -0.722877602
## NRO5.1 .
## NRO6.1 -0.403597861
## NRA1.1 -0.698425538
## NRA2.1 .
## NRA3.1 0.318301970
## NRA4.1 0.037363739
## NRA5.1 0.725843842
## NRA6.1 1.230165485
## NRC1.1 .
## NRC2.1 0.986001571
## NRC3.1 .
## NRC4.1 0.324993444
## NRC5.1 .
## NRC6.1 -0.243623359
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.264706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.77440178
## NRN1.1 0.87620808
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.55965837
## NRE3.1 0.44077361
## NRE4.1 -0.31980933
## NRE5.1 .
## NRE6.1 -0.41349766
## NRO1.1 .
## NRO2.1 -0.23601218
## NRO3.1 .
## NRO4.1 -0.68065973
## NRO5.1 .
## NRO6.1 -0.09670984
## NRA1.1 -0.49013677
## NRA2.1 .
## NRA3.1 0.05568257
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.99098111
## NRC1.1 .
## NRC2.1 1.19347214
## NRC3.1 .
## NRC4.1 0.48284788
## NRC5.1 .
## NRC6.1 .
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.029851
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.6629438
## NRN1.1 0.4935450
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.3471436
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.1677400
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.7291351
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.1470880
## NRC1.1 .
## NRC2.1 0.6651578
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.06583674
## NRN1.1 0.89552409
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.66276134
## NRE3.1 0.36639754
## NRE4.1 -0.35046815
## NRE5.1 .
## NRE6.1 -0.45522827
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.18704420
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.51520574
## NRA2.1 .
## NRA3.1 0.02148018
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.99430749
## NRC1.1 .
## NRC2.1 1.45000202
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## running cv # 10
## running fold # 1
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.552239
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.55933692
## NRN1.1 0.81293591
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.37838586
## NRE3.1 0.65706890
## NRE4.1 -0.85716573
## NRE5.1 .
## NRE6.1 -0.61158755
## NRO1.1 .
## NRO2.1 -0.16930219
## NRO3.1 .
## NRO4.1 -0.53645466
## NRO5.1 .
## NRO6.1 -0.25458493
## NRA1.1 -0.58280193
## NRA2.1 .
## NRA3.1 0.28913250
## NRA4.1 .
## NRA5.1 0.04218002
## NRA6.1 0.83206646
## NRC1.1 .
## NRC2.1 1.05116665
## NRC3.1 .
## NRC4.1 0.45553071
## NRC5.1 .
## NRC6.1 .
## running fold # 2
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.90112726
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.09844773
## NRN4.1 0.01964683
## NRN5.1 .
## NRN6.1 .
## NRE1.1 1.33116361
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 -0.04135458
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.53839869
## NRN1.1 1.22486472
## NRN2.1 0.34342033
## NRN3.1 0.26858175
## NRN4.1 -0.52354862
## NRN5.1 0.43652052
## NRN6.1 -0.25927009
## NRE1.1 -0.60133774
## NRE2.1 -0.45132390
## NRE3.1 1.00079137
## NRE4.1 -0.56371667
## NRE5.1 0.33855170
## NRE6.1 -0.45345575
## NRO1.1 -0.28202548
## NRO2.1 -0.59881642
## NRO3.1 -0.23993536
## NRO4.1 -0.77074141
## NRO5.1 0.49620646
## NRO6.1 -0.62444208
## NRA1.1 -1.24639098
## NRA2.1 -0.09353173
## NRA3.1 0.95746692
## NRA4.1 0.77646244
## NRA5.1 0.59662157
## NRA6.1 1.19944831
## NRC1.1 -0.32134441
## NRC2.1 0.88412456
## NRC3.1 -0.22206438
## NRC4.1 0.83795738
## NRC5.1 0.26687322
## NRC6.1 -0.32045715
## running fold # 3
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.686567
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.583222376
## NRN1.1 0.342523052
## NRN2.1 0.025168240
## NRN3.1 .
## NRN4.1 -0.067701497
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.946318341
## NRE3.1 0.271532457
## NRE4.1 .
## NRE5.1 -0.124958867
## NRE6.1 -0.411595565
## NRO1.1 .
## NRO2.1 -0.338369125
## NRO3.1 .
## NRO4.1 -0.441181572
## NRO5.1 .
## NRO6.1 -0.065416498
## NRA1.1 -0.519491560
## NRA2.1 .
## NRA3.1 0.180309104
## NRA4.1 .
## NRA5.1 0.214471605
## NRA6.1 0.218347038
## NRC1.1 .
## NRC2.1 1.860504221
## NRC3.1 .
## NRC4.1 0.663492203
## NRC5.1 .
## NRC6.1 -0.005157274
## running fold # 4
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.850746
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.72425097
## NRN1.1 0.44539177
## NRN2.1 0.23502103
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.56703310
## NRE3.1 0.53826269
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.76075021
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.39730917
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.96207525
## NRA2.1 .
## NRA3.1 0.64505362
## NRA4.1 .
## NRA5.1 0.10016381
## NRA6.1 1.37873758
## NRC1.1 -0.16856056
## NRC2.1 0.87282727
## NRC3.1 .
## NRC4.1 0.10614227
## NRC5.1 -0.05527984
## NRC6.1 .
## running fold # 5
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.38806
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## 31 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.3212606
## NRN1.1 0.5940630
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.4586854
## NRE3.1 0.2129582
## NRE4.1 -0.2942561
## NRE5.1 .
## NRE6.1 -0.9005309
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.4610098
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.9465607
## NRC1.1 .
## NRC2.1 1.3178656
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .mult.pred.m2.hd = as.data.frame(mult.pred.m2.hd)
mult.pred.m2.hd$mean = rowMeans(cbind(mult.pred.m2.hd$V1, mult.pred.m2.hd$V2, mult.pred.m2.hd$V3, mult.pred.m2.hd$V4, mult.pred.m2.hd$V5,
mult.pred.m2.hd$V6, mult.pred.m2.hd$V7, mult.pred.m2.hd$V8, mult.pred.m2.hd$V9, mult.pred.m2.hd$V10))
mult.pred.m2.hd = cbind(mult.pred.m2.hd, hd.pai)
mult.pred.m2.hd$indication = if_else(mult.pred.m2.hd$mean < 0, 0, 1)
cbt.indicated.m2.hd = filter(mult.pred.m2.hd, indication == 0)
med.indicated.m2.hd = filter(mult.pred.m2.hd, indication == 1)
# Analyses across tx group
mult.pred.m2.hd$match = if_else(mult.pred.m2.hd$indication==mult.pred.m2.hd$txgrp, 0, 1)
describeBy(mult.pred.m2.hd$y, group = mult.pred.m2.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 105 7.01 5.98 6 6.4 5.93 0 26 26 0.82 -0.02 0.58
## Q0.25 Q0.75
## 1 2 11
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 220 9.68 7.81 8 9.05 8.9 0 33 33 0.59 -0.64 0.53
## Q0.25 Q0.75
## 1 3 16t.test(mult.pred.m2.hd$y ~ mult.pred.m2.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m2.hd$y by mult.pred.m2.hd$match
## t = -3.4016, df = 260.47, p-value = 0.0007753
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.219247 -1.125342
## sample estimates:
## mean in group 0 mean in group 1
## 7.009524 9.681818cohen.d(mult.pred.m2.hd$y ~ mult.pred.m2.hd$match)##
## Cohen's d
##
## d estimate: -0.3676297 (small)
## 95 percent confidence interval:
## lower upper
## -0.6027011 -0.1325583#### Model 3 (Demographics and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(hd.pai)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(hd.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(hd.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = hd.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 32:34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:30, 31, 32)] # Fix this
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = hd.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30, 32, 33)]
X_train_med_data = hd.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 32:34)]
X_train_med_enfunc = X_train_med_data[,c(1:30, 31, 32)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = hd.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30, 32, 33)]
hd.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
hd.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(hd.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(hd.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(hd.cbt.model3, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(hd.med.model3, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m3.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.656716
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.62544550
## NRN1.1 0.31196208
## NRN2.1 0.30454411
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.72116898
## NRE3.1 0.61550495
## NRE4.1 -0.45453034
## NRE5.1 0.01534139
## NRE6.1 -1.04890585
## NRO1.1 -0.33477135
## NRO2.1 -0.01230848
## NRO3.1 .
## NRO4.1 -0.40526010
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.95662387
## NRA2.1 .
## NRA3.1 0.06091188
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.86012697
## NRC1.1 .
## NRC2.1 0.79411168
## NRC3.1 .
## NRC4.1 0.48615191
## NRC5.1 .
## NRC6.1 .
## sex 1.11389744
## age 0.21339355
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.691176
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 4.94054883
## NRN1.1 0.94104137
## NRN2.1 0.45544562
## NRN3.1 -0.04811021
## NRN4.1 -0.35753645
## NRN5.1 0.10227812
## NRN6.1 -0.62759933
## NRE1.1 -0.67126281
## NRE2.1 -0.64348388
## NRE3.1 0.94477712
## NRE4.1 -0.57975796
## NRE5.1 -0.32376802
## NRE6.1 -0.35759966
## NRO1.1 0.28055479
## NRO2.1 -0.29434433
## NRO3.1 -0.08906650
## NRO4.1 -0.52683173
## NRO5.1 0.10321464
## NRO6.1 -0.82124650
## NRA1.1 -1.20087073
## NRA2.1 -0.22593363
## NRA3.1 1.03493012
## NRA4.1 0.46153411
## NRA5.1 0.32504299
## NRA6.1 1.00217669
## NRC1.1 -0.62633860
## NRC2.1 1.10705582
## NRC3.1 -0.09488985
## NRC4.1 1.00343678
## NRC5.1 0.08058217
## NRC6.1 -0.22883183
## sex 2.51010921
## age 0.62435995
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.343284
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.36080665
## NRN1.1 0.69646175
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.21436857
## NRE2.1 -0.69063215
## NRE3.1 0.46164263
## NRE4.1 -0.88699376
## NRE5.1 .
## NRE6.1 -0.04221064
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.58908677
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.07641880
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.90144665
## NRC1.1 .
## NRC2.1 1.35283554
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.04556846
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.447761
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.9092426
## NRN1.1 0.9273817
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.6695180
## NRE3.1 1.0092560
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.3828645
## NRO1.1 .
## NRO2.1 -0.6328605
## NRO3.1 .
## NRO4.1 -0.8567764
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.7001295
## NRA2.1 .
## NRA3.1 0.5883687
## NRA4.1 .
## NRA5.1 0.2747163
## NRA6.1 1.0687889
## NRC1.1 .
## NRC2.1 1.0013452
## NRC3.1 .
## NRC4.1 0.2221389
## NRC5.1 .
## NRC6.1 .
## sex 0.3963669
## age 0.1046202
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.41791
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.31778242
## NRN1.1 0.51038568
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.41151299
## NRE3.1 0.83847899
## NRE4.1 -0.49700066
## NRE5.1 .
## NRE6.1 -0.99516422
## NRO1.1 .
## NRO2.1 -0.50391463
## NRO3.1 -0.32237023
## NRO4.1 -0.61331249
## NRO5.1 0.09303868
## NRO6.1 .
## NRA1.1 -1.21055337
## NRA2.1 0.05884443
## NRA3.1 0.05764005
## NRA4.1 0.44054072
## NRA5.1 0.37208537
## NRA6.1 1.07922492
## NRC1.1 .
## NRC2.1 1.72260670
## NRC3.1 .
## NRC4.1 0.51624224
## NRC5.1 .
## NRC6.1 -0.09451448
## sex 1.89395651
## age 0.06226165
## running cv # 2
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.2379840
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.1217908
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.35722142
## NRN1.1 0.86825641
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.43020269
## NRE3.1 0.49692231
## NRE4.1 -0.49068257
## NRE5.1 0.10856387
## NRE6.1 -0.59097947
## NRO1.1 .
## NRO2.1 -0.16828991
## NRO3.1 .
## NRO4.1 -0.42417190
## NRO5.1 .
## NRO6.1 -0.15152788
## NRA1.1 -0.97670853
## NRA2.1 .
## NRA3.1 0.61775844
## NRA4.1 0.10193069
## NRA5.1 .
## NRA6.1 0.93699662
## NRC1.1 .
## NRC2.1 1.29284087
## NRC3.1 .
## NRC4.1 0.06850784
## NRC5.1 .
## NRC6.1 .
## sex 1.41936793
## age 0.08728939
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.462687
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.89685361
## NRN1.1 0.39312594
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.76518033
## NRE3.1 0.39774685
## NRE4.1 -0.09466438
## NRE5.1 .
## NRE6.1 -0.24657298
## NRO1.1 .
## NRO2.1 -0.31194288
## NRO3.1 .
## NRO4.1 -0.33004619
## NRO5.1 .
## NRO6.1 -0.25637470
## NRA1.1 -0.37316782
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.83585277
## NRA6.1 0.95423189
## NRC1.1 .
## NRC2.1 0.75556182
## NRC3.1 .
## NRC4.1 0.05899691
## NRC5.1 .
## NRC6.1 .
## sex 0.58915550
## age .
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.38463765
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.82425899
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.25423230
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.53312821
## NRA2.1 0.17465868
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.06725973
## NRC1.1 .
## NRC2.1 1.11440104
## NRC3.1 .
## NRC4.1 0.69617244
## NRC5.1 .
## NRC6.1 .
## sex 0.03544768
## age .
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.426471
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.000701207
## NRN1.1 0.551429460
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.081809539
## NRN6.1 .
## NRE1.1 -0.300811943
## NRE2.1 -0.880345163
## NRE3.1 1.005076072
## NRE4.1 -0.282173706
## NRE5.1 .
## NRE6.1 -0.618564993
## NRO1.1 .
## NRO2.1 -0.068985401
## NRO3.1 .
## NRO4.1 -0.764190900
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.805018737
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.894491346
## NRC1.1 -0.006654339
## NRC2.1 1.103373738
## NRC3.1 .
## NRC4.1 0.313369949
## NRC5.1 .
## NRC6.1 .
## sex 0.869869262
## age 0.660438913
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.3966415
## NRN1.1 0.7521549
## NRN2.1 0.2892924
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.2064919
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.5195675
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.4293526
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.5627686
## NRA2.1 .
## NRA3.1 0.5728576
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.6354821
## NRC1.1 .
## NRC2.1 0.6872616
## NRC3.1 .
## NRC4.1 0.0404250
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## running cv # 3
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.92276105
## NRN1.1 0.70014645
## NRN2.1 .
## NRN3.1 0.21624321
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.39082422
## NRE2.1 -0.59351458
## NRE3.1 0.40974398
## NRE4.1 -0.32547584
## NRE5.1 0.02873708
## NRE6.1 -0.65146423
## NRO1.1 -0.04676804
## NRO2.1 -0.10194554
## NRO3.1 .
## NRO4.1 -0.20978729
## NRO5.1 .
## NRO6.1 -0.41640802
## NRA1.1 -0.31287397
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.41352902
## NRC1.1 .
## NRC2.1 1.02704980
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex 1.04273971
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.671642
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.87407976
## NRN1.1 0.64017924
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.06636280
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.00163807
## NRE2.1 -1.00762615
## NRE3.1 1.26940600
## NRE4.1 -0.41649471
## NRE5.1 .
## NRE6.1 -0.57539064
## NRO1.1 -0.24356289
## NRO2.1 -0.05014718
## NRO3.1 -0.01518916
## NRO4.1 -0.47070493
## NRO5.1 .
## NRO6.1 -0.02654161
## NRA1.1 -0.94326316
## NRA2.1 .
## NRA3.1 0.47801223
## NRA4.1 .
## NRA5.1 0.32592402
## NRA6.1 1.24858652
## NRC1.1 .
## NRC2.1 0.97896476
## NRC3.1 .
## NRC4.1 0.66210504
## NRC5.1 .
## NRC6.1 -0.22792375
## sex 0.83976743
## age 0.26449548
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.2192748
## NRN1.1 0.6657872
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.2846085
## NRE3.1 0.8212724
## NRE4.1 -0.6489281
## NRE5.1 .
## NRE6.1 -0.4619928
## NRO1.1 .
## NRO2.1 -0.3513501
## NRO3.1 .
## NRO4.1 -1.0371170
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.7237722
## NRA2.1 .
## NRA3.1 0.1609498
## NRA4.1 .
## NRA5.1 0.2984282
## NRA6.1 1.1885264
## NRC1.1 .
## NRC2.1 0.9549960
## NRC3.1 .
## NRC4.1 0.4532046
## NRC5.1 .
## NRC6.1 .
## sex 1.9223132
## age 0.5604258
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.573529
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.958080585
## NRN1.1 0.214348754
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.172679255
## NRE3.1 0.004551074
## NRE4.1 -0.209574939
## NRE5.1 .
## NRE6.1 -0.676086010
## NRO1.1 .
## NRO2.1 -0.151432545
## NRO3.1 .
## NRO4.1 -0.393595762
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.113045349
## NRA2.1 .
## NRA3.1 0.321770338
## NRA4.1 .
## NRA5.1 0.392870621
## NRA6.1 0.781025730
## NRC1.1 .
## NRC2.1 1.357522444
## NRC3.1 .
## NRC4.1 0.692414007
## NRC5.1 .
## NRC6.1 .
## sex 0.730689856
## age 0.172924493
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.90721467
## NRN1.1 .
## NRN2.1 0.18780804
## NRN3.1 0.49074326
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.95746226
## NRE2.1 0.43189785
## NRE3.1 -0.39526601
## NRE4.1 .
## NRE5.1 -0.02426094
## NRE6.1 -0.53014977
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.18879172
## NRO5.1 -0.12259889
## NRO6.1 -0.70609617
## NRA1.1 0.27440128
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.53164626
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.09551417
## NRC2.1 -0.64195187
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.51863917
## NRN1.1 0.79975831
## NRN2.1 0.14672004
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.71494866
## NRE3.1 0.57433076
## NRE4.1 -0.28797946
## NRE5.1 .
## NRE6.1 -0.52195175
## NRO1.1 .
## NRO2.1 -0.25202704
## NRO3.1 -0.08986927
## NRO4.1 -0.47907851
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.04383493
## NRA2.1 .
## NRA3.1 0.26036250
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.85866383
## NRC1.1 .
## NRC2.1 1.10895719
## NRC3.1 .
## NRC4.1 0.46658676
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.16222179
## running cv # 4
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.367647
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.01868304
## NRN1.1 0.16529999
## NRN2.1 0.18397643
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.10773370
## NRE3.1 0.11730957
## NRE4.1 -0.40158015
## NRE5.1 .
## NRE6.1 -0.48288712
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.36669848
## NRO5.1 .
## NRO6.1 -0.17312625
## NRA1.1 -0.05182202
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.22452334
## NRA6.1 0.30814504
## NRC1.1 .
## NRC2.1 0.98027473
## NRC3.1 .
## NRC4.1 0.37153168
## NRC5.1 .
## NRC6.1 .
## sex 0.69774059
## age .
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.27764211
## NRN1.1 0.04230394
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.38083342
## NRN5.1 .
## NRN6.1 .
## NRE1.1 1.07316227
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.01895688
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.18543681
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.05717144
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.12755113
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.935970273
## NRN1.1 0.395549349
## NRN2.1 0.217688094
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.350059111
## NRE2.1 -0.540340962
## NRE3.1 0.481047542
## NRE4.1 -0.426245143
## NRE5.1 .
## NRE6.1 -0.523849707
## NRO1.1 -0.005184535
## NRO2.1 -0.373758183
## NRO3.1 -0.076597458
## NRO4.1 -0.366302092
## NRO5.1 .
## NRO6.1 -0.014834289
## NRA1.1 -1.272692983
## NRA2.1 .
## NRA3.1 0.603430449
## NRA4.1 .
## NRA5.1 0.131517515
## NRA6.1 1.115201395
## NRC1.1 .
## NRC2.1 0.914226498
## NRC3.1 .
## NRC4.1 0.336601963
## NRC5.1 .
## NRC6.1 .
## sex 0.491873936
## age 0.785044862
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.48471074
## NRN1.1 0.65712168
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.74921353
## NRE3.1 0.64345000
## NRE4.1 -0.24533238
## NRE5.1 .
## NRE6.1 -0.50756692
## NRO1.1 .
## NRO2.1 -0.02743909
## NRO3.1 .
## NRO4.1 -0.64379392
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.71435980
## NRA2.1 .
## NRA3.1 0.10215381
## NRA4.1 0.09152789
## NRA5.1 .
## NRA6.1 1.17717336
## NRC1.1 -0.05289545
## NRC2.1 1.20050084
## NRC3.1 .
## NRC4.1 0.57022031
## NRC5.1 .
## NRC6.1 .
## sex 1.08935010
## age 0.42876465
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.761194
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.736596170
## NRN1.1 0.997007302
## NRN2.1 0.503789562
## NRN3.1 -0.051642250
## NRN4.1 -0.350375555
## NRN5.1 0.056640819
## NRN6.1 -0.459013090
## NRE1.1 -0.519081661
## NRE2.1 -0.551597126
## NRE3.1 1.182742105
## NRE4.1 -0.413930140
## NRE5.1 0.004035927
## NRE6.1 -0.750452225
## NRO1.1 -0.056622561
## NRO2.1 -0.794206120
## NRO3.1 -0.050311784
## NRO4.1 -0.658607970
## NRO5.1 0.431871919
## NRO6.1 -0.306723403
## NRA1.1 -1.071528198
## NRA2.1 -0.390361051
## NRA3.1 0.405082243
## NRA4.1 0.398584478
## NRA5.1 0.480556003
## NRA6.1 1.372977693
## NRC1.1 -0.436464604
## NRC2.1 1.433028754
## NRC3.1 -0.174904862
## NRC4.1 0.633859947
## NRC5.1 0.008162379
## NRC6.1 -0.065896415
## sex 1.507187743
## age 0.412675767
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.00101096
## NRN1.1 0.76205762
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.01697406
## NRE2.1 -0.60832128
## NRE3.1 0.16458059
## NRE4.1 -0.07748851
## NRE5.1 .
## NRE6.1 -0.47471209
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.60229195
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.70028974
## NRA2.1 .
## NRA3.1 0.76931782
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.10941395
## NRC1.1 .
## NRC2.1 0.81596632
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex 0.31535983
## age .
## running cv # 5
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.970149
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.921476027
## NRN1.1 0.897643889
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.084199353
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.665998607
## NRE2.1 -0.788043796
## NRE3.1 0.918184766
## NRE4.1 -0.737750473
## NRE5.1 0.426111838
## NRE6.1 -0.252568008
## NRO1.1 -0.080158959
## NRO2.1 -0.587926891
## NRO3.1 .
## NRO4.1 -0.386538296
## NRO5.1 .
## NRO6.1 -0.173687530
## NRA1.1 -0.837306446
## NRA2.1 0.003620745
## NRA3.1 0.370239617
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.187199097
## NRC1.1 .
## NRC2.1 1.443597997
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 -0.227402834
## sex 1.783172811
## age 0.622628569
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.313433
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.07940252
## NRN1.1 0.87272536
## NRN2.1 0.22608706
## NRN3.1 -0.42125185
## NRN4.1 -0.30429085
## NRN5.1 0.19395886
## NRN6.1 -0.18531512
## NRE1.1 -0.36698895
## NRE2.1 -0.93672182
## NRE3.1 0.91060109
## NRE4.1 -0.67565087
## NRE5.1 0.16933511
## NRE6.1 -0.76478633
## NRO1.1 0.26672628
## NRO2.1 -0.67001433
## NRO3.1 -0.35599101
## NRO4.1 -0.69987201
## NRO5.1 0.47109292
## NRO6.1 -0.31280773
## NRA1.1 -1.15700263
## NRA2.1 -0.42552567
## NRA3.1 1.11516171
## NRA4.1 0.58197277
## NRA5.1 0.11838011
## NRA6.1 1.46598597
## NRC1.1 -0.26331878
## NRC2.1 1.13725662
## NRC3.1 -0.17863868
## NRC4.1 0.79885878
## NRC5.1 -0.11029182
## NRC6.1 0.01859357
## sex 1.51130833
## age 0.79773114
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.7847968
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.1909846
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.5837171
## NRN1.1 0.7019730
## NRN2.1 0.4177362
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.3048731
## NRE3.1 0.5518250
## NRE4.1 -0.3918044
## NRE5.1 .
## NRE6.1 -0.5369124
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.7780246
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.5778116
## NRA2.1 .
## NRA3.1 0.1012185
## NRA4.1 .
## NRA5.1 0.3885243
## NRA6.1 0.6980564
## NRC1.1 -0.1054268
## NRC2.1 0.8396964
## NRC3.1 .
## NRC4.1 0.7751183
## NRC5.1 -0.1707341
## NRC6.1 .
## sex 1.5086740
## age 0.1340983
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.01393515
## NRN1.1 0.45044198
## NRN2.1 .
## NRN3.1 0.15218281
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.26147819
## NRE2.1 -0.14259975
## NRE3.1 0.82311576
## NRE4.1 -0.12785920
## NRE5.1 .
## NRE6.1 -0.94743297
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.37748653
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.02653351
## NRA2.1 .
## NRA3.1 0.21339793
## NRA4.1 .
## NRA5.1 0.18573315
## NRA6.1 0.94524109
## NRC1.1 .
## NRC2.1 1.19722812
## NRC3.1 .
## NRC4.1 0.07992551
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.08871616
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.00214071
## NRN1.1 0.26931549
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.45158128
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.59505798
## NRO1.1 -0.18408136
## NRO2.1 -0.07280988
## NRO3.1 .
## NRO4.1 -0.39056867
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.57037040
## NRA2.1 .
## NRA3.1 0.13470149
## NRA4.1 .
## NRA5.1 0.09276458
## NRA6.1 0.27316546
## NRC1.1 .
## NRC2.1 1.09337429
## NRC3.1 .
## NRC4.1 0.48154597
## NRC5.1 .
## NRC6.1 .
## sex 0.84768563
## age .
## running cv # 6
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.59829174
## NRN1.1 1.02981376
## NRN2.1 0.16507947
## NRN3.1 -0.13317463
## NRN4.1 -0.57764018
## NRN5.1 -0.17808837
## NRN6.1 -0.50954516
## NRE1.1 -0.87819454
## NRE2.1 -1.13143559
## NRE3.1 1.14724080
## NRE4.1 -0.83379138
## NRE5.1 0.27759213
## NRE6.1 -0.80702473
## NRO1.1 0.05680142
## NRO2.1 -0.49405185
## NRO3.1 -0.04879014
## NRO4.1 -0.31943494
## NRO5.1 0.44903390
## NRO6.1 -0.52991133
## NRA1.1 -0.85789741
## NRA2.1 0.23231939
## NRA3.1 0.74387308
## NRA4.1 0.48402674
## NRA5.1 0.28291836
## NRA6.1 1.06718548
## NRC1.1 -0.42637629
## NRC2.1 1.17612269
## NRC3.1 -0.29756594
## NRC4.1 0.76351001
## NRC5.1 -0.02494307
## NRC6.1 -0.31376307
## sex 1.68563341
## age 0.26493561
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.764706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.0428823987
## NRN1.1 0.7136889843
## NRN2.1 0.3210951886
## NRN3.1 0.1458935481
## NRN4.1 -0.1121531046
## NRN5.1 0.3188978759
## NRN6.1 -0.2520458264
## NRE1.1 -0.5822095124
## NRE2.1 -1.0995202737
## NRE3.1 1.1543098784
## NRE4.1 -0.5682894859
## NRE5.1 0.8149287271
## NRE6.1 -0.7411037938
## NRO1.1 -0.3800394457
## NRO2.1 -0.5949643056
## NRO3.1 0.0450146924
## NRO4.1 -0.5123894771
## NRO5.1 0.4128913654
## NRO6.1 -0.3060404099
## NRA1.1 -1.2898987026
## NRA2.1 -0.0008185183
## NRA3.1 0.8445106521
## NRA4.1 0.2019180672
## NRA5.1 0.3126569044
## NRA6.1 0.9658554619
## NRC1.1 -0.2618390454
## NRC2.1 1.3102521297
## NRC3.1 -0.3532919725
## NRC4.1 0.4868569444
## NRC5.1 0.0293401734
## NRC6.1 -0.2281717186
## sex 1.9942006426
## age 0.5452838521
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.880597
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.12844428
## NRN1.1 0.08310977
## NRN2.1 0.13310390
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 0.55654488
## NRE4.1 -0.54925207
## NRE5.1 .
## NRE6.1 -0.38285911
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -1.57185100
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.80639198
## NRA2.1 .
## NRA3.1 0.32913446
## NRA4.1 .
## NRA5.1 0.11021176
## NRA6.1 1.16139196
## NRC1.1 .
## NRC2.1 0.97662132
## NRC3.1 .
## NRC4.1 0.31672975
## NRC5.1 .
## NRC6.1 .
## sex 1.27302330
## age 0.21809400
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.80983229
## NRN1.1 1.01700714
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.06193310
## NRE3.1 0.45110926
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.80434666
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.37474506
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.44692126
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.06143618
## NRA6.1 0.78813736
## NRC1.1 .
## NRC2.1 1.40211288
## NRC3.1 .
## NRC4.1 0.09920572
## NRC5.1 .
## NRC6.1 .
## sex .
## age 0.05755290
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.81239022
## NRN1.1 0.80350674
## NRN2.1 0.09469582
## NRN3.1 0.07256782
## NRN4.1 -0.13530947
## NRN5.1 -0.33572712
## NRN6.1 0.08656710
## NRE1.1 -0.52141593
## NRE2.1 -0.59450742
## NRE3.1 0.78275071
## NRE4.1 -0.53319790
## NRE5.1 -0.28258317
## NRE6.1 -0.34909901
## NRO1.1 -0.03769693
## NRO2.1 -0.75857799
## NRO3.1 -0.22354012
## NRO4.1 -0.66812231
## NRO5.1 0.64831169
## NRO6.1 -0.22342818
## NRA1.1 -1.18174124
## NRA2.1 0.11280318
## NRA3.1 0.64113307
## NRA4.1 0.09624538
## NRA5.1 0.24094063
## NRA6.1 1.28571952
## NRC1.1 -0.29374426
## NRC2.1 1.61263387
## NRC3.1 -0.13593626
## NRC4.1 0.85734336
## NRC5.1 -0.01575321
## NRC6.1 -0.53819951
## sex 2.03855111
## age 0.58083339
## running cv # 7
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.179104
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.336712483
## NRN1.1 1.234947734
## NRN2.1 0.063238510
## NRN3.1 -0.109989589
## NRN4.1 -0.009669502
## NRN5.1 -0.184319066
## NRN6.1 -0.349520443
## NRE1.1 -0.640983569
## NRE2.1 -1.054125961
## NRE3.1 1.111285675
## NRE4.1 -0.799529215
## NRE5.1 0.381473225
## NRE6.1 -0.990095179
## NRO1.1 -0.233493291
## NRO2.1 -0.299566035
## NRO3.1 0.255727999
## NRO4.1 -0.437161989
## NRO5.1 0.493467343
## NRO6.1 -0.471329601
## NRA1.1 -0.940949593
## NRA2.1 0.133738950
## NRA3.1 0.587064596
## NRA4.1 0.326456674
## NRA5.1 0.237898974
## NRA6.1 1.066327963
## NRC1.1 -0.540217481
## NRC2.1 0.820262171
## NRC3.1 -0.014142841
## NRC4.1 0.765201823
## NRC5.1 0.225807398
## NRC6.1 -0.383797639
## sex 1.185340276
## age 0.401741923
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.40061360
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.08645242
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.04168647
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.64877309
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.37092091
## NRC1.1 -0.10591726
## NRC2.1 -1.33269404
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.27167279
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.12767724
## NRN1.1 0.47548329
## NRN2.1 0.35251444
## NRN3.1 0.29144751
## NRN4.1 -0.07455256
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.60523431
## NRE3.1 1.18673489
## NRE4.1 -0.97767703
## NRE5.1 .
## NRE6.1 -0.46502803
## NRO1.1 -0.17103639
## NRO2.1 -0.74278973
## NRO3.1 -0.42178042
## NRO4.1 -0.33304437
## NRO5.1 .
## NRO6.1 -0.46709317
## NRA1.1 -0.99779058
## NRA2.1 -0.11867309
## NRA3.1 0.71603970
## NRA4.1 .
## NRA5.1 0.55972624
## NRA6.1 1.80254248
## NRC1.1 -0.76927584
## NRC2.1 1.39391693
## NRC3.1 -0.08417881
## NRC4.1 0.81627019
## NRC5.1 .
## NRC6.1 -0.08855970
## sex 0.66661485
## age 0.01405523
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.279412
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.863257637
## NRN1.1 0.976028958
## NRN2.1 0.414057630
## NRN3.1 -0.262794877
## NRN4.1 -0.152799082
## NRN5.1 -0.105672446
## NRN6.1 -0.381749229
## NRE1.1 -0.551770058
## NRE2.1 -0.427507935
## NRE3.1 0.753260379
## NRE4.1 -0.550767751
## NRE5.1 0.158397594
## NRE6.1 -0.606937674
## NRO1.1 0.354408223
## NRO2.1 -0.291692355
## NRO3.1 -0.270129409
## NRO4.1 -0.776390471
## NRO5.1 0.214719853
## NRO6.1 -0.368112431
## NRA1.1 -1.042360594
## NRA2.1 0.068702538
## NRA3.1 0.530747203
## NRA4.1 0.387442826
## NRA5.1 0.321608857
## NRA6.1 1.120411704
## NRC1.1 0.001839119
## NRC2.1 1.369857516
## NRC3.1 -0.120326443
## NRC4.1 0.840648473
## NRC5.1 -0.213486119
## NRC6.1 -0.413756495
## sex 1.626228290
## age 0.421110835
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.089552
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.207024092
## NRN1.1 .
## NRN2.1 0.009110821
## NRN3.1 .
## NRN4.1 -0.185301338
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.423163587
## NRE3.1 1.052365572
## NRE4.1 -0.154036254
## NRE5.1 .
## NRE6.1 -1.123347309
## NRO1.1 .
## NRO2.1 -0.665864538
## NRO3.1 .
## NRO4.1 -0.904616594
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.061731452
## NRA2.1 .
## NRA3.1 0.517089946
## NRA4.1 0.248170165
## NRA5.1 .
## NRA6.1 0.809906334
## NRC1.1 .
## NRC2.1 1.484657720
## NRC3.1 .
## NRC4.1 0.369069166
## NRC5.1 .
## NRC6.1 .
## sex 1.981184919
## age 0.280497233
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.12781577
## NRN1.1 0.97439694
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 0.06736310
## NRN6.1 .
## NRE1.1 -0.16956868
## NRE2.1 -0.73378713
## NRE3.1 0.76333498
## NRE4.1 -0.58535875
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 -0.13520175
## NRO3.1 .
## NRO4.1 -0.80784659
## NRO5.1 0.37699866
## NRO6.1 .
## NRA1.1 -0.87796233
## NRA2.1 .
## NRA3.1 0.08684043
## NRA4.1 .
## NRA5.1 0.18605969
## NRA6.1 0.74194699
## NRC1.1 .
## NRC2.1 1.44056478
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex 1.45617752
## age 0.62645797
## running cv # 8
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.161765
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.07162435
## NRN1.1 0.48628721
## NRN2.1 0.60587582
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.17064621
## NRE2.1 -0.62076984
## NRE3.1 0.91644676
## NRE4.1 -0.48600801
## NRE5.1 .
## NRE6.1 -0.60991946
## NRO1.1 .
## NRO2.1 -0.28347032
## NRO3.1 -0.07280423
## NRO4.1 -0.85788869
## NRO5.1 .
## NRO6.1 -0.16406321
## NRA1.1 -1.07245341
## NRA2.1 .
## NRA3.1 0.74080738
## NRA4.1 0.14612360
## NRA5.1 .
## NRA6.1 1.35283791
## NRC1.1 .
## NRC2.1 1.06675517
## NRC3.1 .
## NRC4.1 0.26682729
## NRC5.1 .
## NRC6.1 -0.02080935
## sex 0.77914234
## age 0.41036564
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.104478
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.734914284
## NRN1.1 0.491789457
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.584003047
## NRE3.1 0.114612857
## NRE4.1 -0.207214986
## NRE5.1 .
## NRE6.1 -0.477518563
## NRO1.1 .
## NRO2.1 -0.485448227
## NRO3.1 .
## NRO4.1 -0.334327022
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.847437546
## NRA2.1 .
## NRA3.1 0.123931023
## NRA4.1 .
## NRA5.1 0.238892812
## NRA6.1 0.605457967
## NRC1.1 .
## NRC2.1 1.074894427
## NRC3.1 .
## NRC4.1 0.520492218
## NRC5.1 .
## NRC6.1 .
## sex 0.734758008
## age 0.001194934
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.074627
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.7839756
## NRN1.1 0.3487359
## NRN2.1 0.2634223
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.6073953
## NRE3.1 1.2461521
## NRE4.1 -0.7920811
## NRE5.1 0.2190698
## NRE6.1 -0.8535377
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.7240274
## NRO5.1 .
## NRO6.1 -0.4149720
## NRA1.1 -0.6242951
## NRA2.1 .
## NRA3.1 0.1900892
## NRA4.1 .
## NRA5.1 0.6932659
## NRA6.1 0.8780009
## NRC1.1 .
## NRC2.1 1.4805845
## NRC3.1 .
## NRC4.1 0.7385339
## NRC5.1 .
## NRC6.1 -0.3748982
## sex 1.5376163
## age 0.2889585
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.21523566
## NRN1.1 0.48107244
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.32346999
## NRE3.1 0.34788193
## NRE4.1 -0.64064902
## NRE5.1 .
## NRE6.1 -0.41381946
## NRO1.1 -0.11063100
## NRO2.1 -0.04819361
## NRO3.1 .
## NRO4.1 -0.17902175
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.90246789
## NRA2.1 .
## NRA3.1 0.01485811
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.52123466
## NRC1.1 .
## NRC2.1 1.07800053
## NRC3.1 .
## NRC4.1 0.04947831
## NRC5.1 .
## NRC6.1 .
## sex 0.68614645
## age 0.31790388
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.223881
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.96476655
## NRN1.1 0.98295188
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.27902991
## NRE2.1 -0.61462343
## NRE3.1 0.58597342
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.67811409
## NRO1.1 .
## NRO2.1 -0.02254359
## NRO3.1 -0.16422941
## NRO4.1 -0.59107577
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.39981257
## NRA2.1 .
## NRA3.1 0.46575085
## NRA4.1 .
## NRA5.1 0.07782555
## NRA6.1 1.14963977
## NRC1.1 .
## NRC2.1 0.92702354
## NRC3.1 .
## NRC4.1 0.27770997
## NRC5.1 .
## NRC6.1 .
## sex 0.60992926
## age 0.00671948
## running cv # 9
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.865672
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.85410133
## NRN1.1 0.31753069
## NRN2.1 0.47444846
## NRN3.1 0.32752172
## NRN4.1 -0.25028399
## NRN5.1 -0.08884977
## NRN6.1 -0.24869650
## NRE1.1 -0.56962049
## NRE2.1 -0.40305543
## NRE3.1 1.03055258
## NRE4.1 -0.80028364
## NRE5.1 -0.09008165
## NRE6.1 -0.71741662
## NRO1.1 -0.12112140
## NRO2.1 -0.73857482
## NRO3.1 -0.17955206
## NRO4.1 -0.53613261
## NRO5.1 0.83149998
## NRO6.1 -0.50460777
## NRA1.1 -0.89585168
## NRA2.1 0.21823989
## NRA3.1 0.82639044
## NRA4.1 0.11333151
## NRA5.1 0.22585308
## NRA6.1 0.71342821
## NRC1.1 -0.15833941
## NRC2.1 1.30035047
## NRC3.1 -0.10684331
## NRC4.1 0.59095075
## NRC5.1 -0.26928448
## NRC6.1 0.24616222
## sex 1.97779904
## age 0.42050751
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.223881
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.987886780
## NRN1.1 0.966680934
## NRN2.1 0.232180845
## NRN3.1 -0.383209875
## NRN4.1 -0.234530843
## NRN5.1 0.229262943
## NRN6.1 -0.356013758
## NRE1.1 -0.419981295
## NRE2.1 -0.779297182
## NRE3.1 1.093972716
## NRE4.1 -0.644078025
## NRE5.1 0.240910215
## NRE6.1 -0.653949372
## NRO1.1 -0.372766807
## NRO2.1 -0.642628894
## NRO3.1 0.185900638
## NRO4.1 -0.885950198
## NRO5.1 0.316031708
## NRO6.1 -0.579920065
## NRA1.1 -1.002849399
## NRA2.1 0.141666399
## NRA3.1 0.712232325
## NRA4.1 0.479836092
## NRA5.1 0.766292711
## NRA6.1 1.342974258
## NRC1.1 -0.461705317
## NRC2.1 1.116265916
## NRC3.1 -0.166475246
## NRC4.1 0.638801821
## NRC5.1 0.004006129
## NRC6.1 -0.472215953
## sex 1.315582819
## age 0.433623928
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.264706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.006891399
## NRN1.1 0.817220187
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.548579357
## NRE3.1 0.525869925
## NRE4.1 -0.357831472
## NRE5.1 .
## NRE6.1 -0.380546135
## NRO1.1 .
## NRO2.1 -0.254519571
## NRO3.1 .
## NRO4.1 -0.773933390
## NRO5.1 0.003049041
## NRO6.1 -0.066598176
## NRA1.1 -0.690388955
## NRA2.1 .
## NRA3.1 0.072748517
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.068456858
## NRC1.1 .
## NRC2.1 1.194852592
## NRC3.1 .
## NRC4.1 0.490241904
## NRC5.1 .
## NRC6.1 .
## sex 0.463284009
## age 0.423459237
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.029851
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.931080342
## NRN1.1 0.543525065
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.481097483
## NRE3.1 0.005722652
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.410807978
## NRO1.1 .
## NRO2.1 -0.037677836
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.009924767
## NRA2.1 .
## NRA3.1 0.028308131
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.433422401
## NRC1.1 .
## NRC2.1 0.835706354
## NRC3.1 .
## NRC4.1 0.165176990
## NRC5.1 .
## NRC6.1 .
## sex 0.451948816
## age 0.221058468
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.958525625
## NRN1.1 0.839315371
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.652844652
## NRE3.1 0.406429725
## NRE4.1 -0.349894088
## NRE5.1 .
## NRE6.1 -0.483346899
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.293584770
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.489859612
## NRA2.1 .
## NRA3.1 0.007982915
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.980543765
## NRC1.1 .
## NRC2.1 1.407745282
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex 0.698947908
## age .
## running cv # 10
## running fold # 1
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.552239
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.05651586
## NRN1.1 0.75411689
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.40209196
## NRE3.1 0.74496636
## NRE4.1 -0.88424674
## NRE5.1 .
## NRE6.1 -0.61589845
## NRO1.1 .
## NRO2.1 -0.21153123
## NRO3.1 .
## NRO4.1 -0.65643628
## NRO5.1 .
## NRO6.1 -0.22631666
## NRA1.1 -0.61192139
## NRA2.1 .
## NRA3.1 0.27533754
## NRA4.1 .
## NRA5.1 0.04236769
## NRA6.1 0.86415244
## NRC1.1 .
## NRC2.1 1.04819366
## NRC3.1 .
## NRC4.1 0.42368023
## NRC5.1 .
## NRC6.1 .
## sex 0.91382126
## age 0.08747712
## running fold # 2
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.90112726
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.09844773
## NRN4.1 0.01964683
## NRN5.1 .
## NRN6.1 .
## NRE1.1 1.33116361
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 -0.04135458
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.82221952
## NRN1.1 1.17000825
## NRN2.1 0.31351015
## NRN3.1 0.30306268
## NRN4.1 -0.51336550
## NRN5.1 0.34839649
## NRN6.1 -0.32078286
## NRE1.1 -0.69235590
## NRE2.1 -0.41892645
## NRE3.1 1.01700114
## NRE4.1 -0.50884076
## NRE5.1 0.48104591
## NRE6.1 -0.47323760
## NRO1.1 -0.20137794
## NRO2.1 -0.62163440
## NRO3.1 -0.22250995
## NRO4.1 -0.85991619
## NRO5.1 0.56400915
## NRO6.1 -0.60400734
## NRA1.1 -1.30502679
## NRA2.1 -0.06316387
## NRA3.1 0.99250595
## NRA4.1 0.73937606
## NRA5.1 0.56321638
## NRA6.1 1.16962926
## NRC1.1 -0.38624346
## NRC2.1 0.87586128
## NRC3.1 -0.30921748
## NRC4.1 0.82238914
## NRC5.1 0.23926967
## NRC6.1 -0.28684299
## sex 1.06400453
## age 0.43370744
## running fold # 3
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.686567
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.97121867
## NRN1.1 0.31134579
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.10587376
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -1.03433873
## NRE3.1 0.35503537
## NRE4.1 .
## NRE5.1 -0.02760442
## NRE6.1 -0.40000254
## NRO1.1 .
## NRO2.1 -0.40680685
## NRO3.1 .
## NRO4.1 -0.63949960
## NRO5.1 .
## NRO6.1 -0.04224303
## NRA1.1 -0.60028991
## NRA2.1 0.06196570
## NRA3.1 0.19948282
## NRA4.1 .
## NRA5.1 0.15865329
## NRA6.1 0.24065284
## NRC1.1 .
## NRC2.1 1.88205097
## NRC3.1 .
## NRC4.1 0.56783784
## NRC5.1 .
## NRC6.1 -0.05760304
## sex 1.60806017
## age 0.07883852
## running fold # 4
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.850746
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.40614728
## NRN1.1 0.41383282
## NRN2.1 0.22160579
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.48657430
## NRE3.1 0.44159506
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.70155216
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.37860763
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.98529522
## NRA2.1 .
## NRA3.1 0.57293447
## NRA4.1 .
## NRA5.1 0.07002460
## NRA6.1 1.30053093
## NRC1.1 -0.07778047
## NRC2.1 0.80945900
## NRC3.1 .
## NRC4.1 0.02755545
## NRC5.1 .
## NRC6.1 .
## sex 0.19285688
## age 0.26124053
## running fold # 5
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.38806
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## sex .
## age .
## 33 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.620313754
## NRN1.1 0.546521817
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.009007017
## NRE2.1 -0.527947841
## NRE3.1 0.403794006
## NRE4.1 -0.494227864
## NRE5.1 .
## NRE6.1 -0.834577803
## NRO1.1 .
## NRO2.1 -0.032298165
## NRO3.1 .
## NRO4.1 -0.088924091
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.601461328
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.915506238
## NRC1.1 .
## NRC2.1 1.139706421
## NRC3.1 .
## NRC4.1 0.145648661
## NRC5.1 .
## NRC6.1 .
## sex 1.073652163
## age 0.292834132mult.pred.m3.hd = as.data.frame(mult.pred.m3.hd)
mult.pred.m3.hd$mean = rowMeans(cbind(mult.pred.m3.hd$V1, mult.pred.m3.hd$V2, mult.pred.m3.hd$V3, mult.pred.m3.hd$V4, mult.pred.m3.hd$V5,
mult.pred.m3.hd$V6, mult.pred.m3.hd$V7, mult.pred.m3.hd$V8, mult.pred.m3.hd$V9, mult.pred.m3.hd$V10))
mult.pred.m3.hd = cbind(mult.pred.m3.hd, hd.pai)
mult.pred.m3.hd$indication = if_else(mult.pred.m3.hd$mean < 0, 0, 1)
cbt.indicated.m3.hd = filter(mult.pred.m3.hd, indication == 0)
med.indicated.m3.hd = filter(mult.pred.m3.hd, indication == 1)
# Analyses across tx group
mult.pred.m3.hd$match = if_else(mult.pred.m3.hd$indication==mult.pred.m3.hd$txgrp, 0, 1)
describeBy(mult.pred.m3.hd$y, group = mult.pred.m3.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 108 6.92 5.88 5.5 6.31 6.67 0 26 26 0.84 0.11 0.57
## Q0.25 Q0.75
## 1 2 11
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 217 9.76 7.84 8 9.14 8.9 0 33 33 0.57 -0.68 0.53
## Q0.25 Q0.75
## 1 3 16t.test(mult.pred.m3.hd$y ~ mult.pred.m3.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m3.hd$y by mult.pred.m3.hd$match
## t = -3.6662, df = 274.04, p-value = 0.0002956
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.377780 -1.318841
## sample estimates:
## mean in group 0 mean in group 1
## 6.916667 9.764977cohen.d(mult.pred.m3.hd$y ~ mult.pred.m3.hd$match)##
## Cohen's d
##
## d estimate: -0.3927365 (small)
## 95 percent confidence interval:
## lower upper
## -0.6263848 -0.1590882#### Model 4 (Pre-tx HAM-D and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(hd.pai)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(hd.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(hd.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = hd.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:31, 34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:31)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = hd.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:31)]
X_train_med_data = hd.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:31, 34)]
X_train_med_enfunc = X_train_med_data[,c(1:31)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = hd.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:31)]
hd.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
hd.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(hd.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(hd.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(hd.cbt.model4, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(hd.med.model4, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m4.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.656716
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.49703604
## NRN1.1 .
## NRN2.1 0.32632391
## NRN3.1 .
## NRN4.1 -0.14193350
## NRN5.1 .
## NRN6.1 -0.08623066
## NRE1.1 .
## NRE2.1 -0.77407108
## NRE3.1 0.44309578
## NRE4.1 -0.50880812
## NRE5.1 0.02723397
## NRE6.1 -1.06711221
## NRO1.1 -0.17906196
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.17793303
## NRO5.1 .
## NRO6.1 -0.07497042
## NRA1.1 -1.04037438
## NRA2.1 .
## NRA3.1 0.10674693
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.84433009
## NRC1.1 .
## NRC2.1 0.65400596
## NRC3.1 .
## NRC4.1 0.66941592
## NRC5.1 .
## NRC6.1 .
## hamd1 1.63056395
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.691176
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.11710794
## NRN1.1 0.34869387
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.71410321
## NRE3.1 .
## NRE4.1 -0.06430854
## NRE5.1 -0.12044152
## NRE6.1 -0.26025580
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.34537484
## NRA1.1 -0.85989540
## NRA2.1 .
## NRA3.1 0.46994261
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.43463935
## NRC1.1 .
## NRC2.1 0.70930726
## NRC3.1 .
## NRC4.1 0.44833670
## NRC5.1 .
## NRC6.1 .
## hamd1 1.72861458
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.343284
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.27250312
## NRN1.1 0.25960097
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.01878386
## NRE1.1 -0.45139419
## NRE2.1 -0.63698155
## NRE3.1 0.56048071
## NRE4.1 -0.97899464
## NRE5.1 0.14278159
## NRE6.1 -0.30027863
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.50316186
## NRO5.1 0.28183641
## NRO6.1 -0.04642024
## NRA1.1 -0.25519524
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.95393635
## NRC1.1 .
## NRC2.1 1.18335891
## NRC3.1 .
## NRC4.1 0.24450009
## NRC5.1 .
## NRC6.1 .
## hamd1 1.77544812
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.447761
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.582059279
## NRN1.1 0.571994753
## NRN2.1 .
## NRN3.1 -0.037695886
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.738627345
## NRE3.1 0.804308869
## NRE4.1 -0.005523117
## NRE5.1 .
## NRE6.1 -0.399673805
## NRO1.1 .
## NRO2.1 -0.500061788
## NRO3.1 .
## NRO4.1 -0.797741742
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.748224549
## NRA2.1 -0.025430603
## NRA3.1 0.637811939
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.017525770
## NRC1.1 .
## NRC2.1 0.867196734
## NRC3.1 .
## NRC4.1 0.148650346
## NRC5.1 .
## NRC6.1 .
## hamd1 1.977374777
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.41791
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.3188742
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.1995573
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.4195613
## NRE3.1 0.5967414
## NRE4.1 -0.4106290
## NRE5.1 .
## NRE6.1 -0.9690402
## NRO1.1 .
## NRO2.1 -0.2513332
## NRO3.1 -0.3963928
## NRO4.1 -0.3443438
## NRO5.1 0.1586477
## NRO6.1 .
## NRA1.1 -1.3643230
## NRA2.1 .
## NRA3.1 0.1662759
## NRA4.1 0.4900421
## NRA5.1 0.2300441
## NRA6.1 1.0356624
## NRC1.1 .
## NRC2.1 1.5922916
## NRC3.1 .
## NRC4.1 0.5665271
## NRC5.1 -0.1173865
## NRC6.1 .
## hamd1 2.2509463
## running cv # 2
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.136835787
## NRN1.1 0.255367052
## NRN2.1 .
## NRN3.1 0.067952118
## NRN4.1 0.234828570
## NRN5.1 -0.107011041
## NRN6.1 .
## NRE1.1 0.774826145
## NRE2.1 0.587909402
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.016009329
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.197750660
## NRA1.1 .
## NRA2.1 -0.006461883
## NRA3.1 .
## NRA4.1 0.684823573
## NRA5.1 .
## NRA6.1 0.185821900
## NRC1.1 .
## NRC2.1 -0.656456046
## NRC3.1 .
## NRC4.1 0.216688375
## NRC5.1 -0.344696213
## NRC6.1 0.493782057
## hamd1 0.446871034
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.76790086
## NRN1.1 0.39122118
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.03185928
## NRE1.1 .
## NRE2.1 -0.49491012
## NRE3.1 0.37806892
## NRE4.1 -0.44334780
## NRE5.1 0.04682782
## NRE6.1 -0.56277770
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.16401083
## NRO5.1 .
## NRO6.1 -0.10035219
## NRA1.1 -1.11218445
## NRA2.1 .
## NRA3.1 0.57644220
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.92961954
## NRC1.1 .
## NRC2.1 1.33219539
## NRC3.1 .
## NRC4.1 0.01045952
## NRC5.1 .
## NRC6.1 .
## hamd1 1.77363200
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.462687
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.812079202
## NRN1.1 0.010569915
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.739100304
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.266604408
## NRO1.1 .
## NRO2.1 -0.000712024
## NRO3.1 .
## NRO4.1 -0.099330345
## NRO5.1 .
## NRO6.1 -0.158065742
## NRA1.1 -0.297367600
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.540885666
## NRA6.1 0.672184826
## NRC1.1 .
## NRC2.1 0.609771665
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 1.669436074
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.458851531
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.290729407
## NRE1.1 .
## NRE2.1 -0.116693041
## NRE3.1 0.007838176
## NRE4.1 -0.459145819
## NRE5.1 .
## NRE6.1 -0.679604053
## NRO1.1 -0.022975537
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.226208803
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.884323212
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.265850467
## NRC1.1 .
## NRC2.1 0.999891424
## NRC3.1 .
## NRC4.1 0.989203265
## NRC5.1 .
## NRC6.1 .
## hamd1 2.627955878
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.426471
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.2768010
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.1143794
## NRE2.1 -1.1683508
## NRE3.1 0.7089234
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.6263031
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.5239201
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.6857505
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.6911787
## NRC1.1 .
## NRC2.1 0.9522828
## NRC3.1 .
## NRC4.1 0.1709144
## NRC5.1 .
## NRC6.1 .
## hamd1 2.0359876
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.42613757
## NRN1.1 0.40655901
## NRN2.1 0.28281713
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.24043333
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.58862069
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.42324030
## NRO5.1 0.07299571
## NRO6.1 .
## NRA1.1 -0.63827732
## NRA2.1 .
## NRA3.1 0.57415505
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.62530965
## NRC1.1 .
## NRC2.1 0.58838435
## NRC3.1 .
## NRC4.1 0.05127127
## NRC5.1 .
## NRC6.1 .
## hamd1 1.35386382
## running cv # 3
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.629001705
## NRN1.1 0.470131506
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.113972992
## NRE1.1 -0.328983789
## NRE2.1 -0.861957596
## NRE3.1 0.538085403
## NRE4.1 -0.508923107
## NRE5.1 0.236834398
## NRE6.1 -0.801470125
## NRO1.1 -0.076046916
## NRO2.1 -0.018385144
## NRO3.1 .
## NRO4.1 -0.002853414
## NRO5.1 .
## NRO6.1 -0.658802929
## NRA1.1 -0.505106861
## NRA2.1 .
## NRA3.1 0.086717745
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.473882017
## NRC1.1 .
## NRC2.1 0.856701254
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.213832415
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.671642
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.324468978
## NRN1.1 0.549426731
## NRN2.1 0.010747254
## NRN3.1 .
## NRN4.1 -0.249197328
## NRN5.1 .
## NRN6.1 -0.333260515
## NRE1.1 .
## NRE2.1 -1.042559415
## NRE3.1 1.146875495
## NRE4.1 -0.498158386
## NRE5.1 .
## NRE6.1 -0.676479239
## NRO1.1 -0.200814914
## NRO2.1 -0.004064276
## NRO3.1 -0.157217179
## NRO4.1 -0.191962192
## NRO5.1 .
## NRO6.1 -0.100872613
## NRA1.1 -1.035380424
## NRA2.1 .
## NRA3.1 0.494468684
## NRA4.1 .
## NRA5.1 0.215706059
## NRA6.1 1.350582829
## NRC1.1 .
## NRC2.1 0.884008664
## NRC3.1 .
## NRC4.1 0.709770504
## NRC5.1 .
## NRC6.1 -0.165940670
## hamd1 1.817595944
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.29272245
## NRN1.1 0.06799744
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.33952652
## NRE3.1 0.19200990
## NRE4.1 -0.26168753
## NRE5.1 .
## NRE6.1 -0.53243478
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.60792687
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.59588637
## NRA2.1 .
## NRA3.1 0.06644832
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.91341455
## NRC1.1 .
## NRC2.1 0.96978189
## NRC3.1 .
## NRC4.1 0.41970101
## NRC5.1 .
## NRC6.1 .
## hamd1 2.05157896
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.573529
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.16762994
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.20487921
## NRE3.1 .
## NRE4.1 -0.29678243
## NRE5.1 .
## NRE6.1 -0.86303299
## NRO1.1 .
## NRO2.1 -0.04358799
## NRO3.1 .
## NRO4.1 -0.18702632
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.11667584
## NRA2.1 .
## NRA3.1 0.22043013
## NRA4.1 .
## NRA5.1 0.08915659
## NRA6.1 0.82666466
## NRC1.1 .
## NRC2.1 1.50086705
## NRC3.1 .
## NRC4.1 0.65961527
## NRC5.1 .
## NRC6.1 .
## hamd1 1.75317918
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.90375753
## NRN1.1 .
## NRN2.1 0.22310694
## NRN3.1 0.42837271
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.86239524
## NRE2.1 0.42737876
## NRE3.1 -0.45185430
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.60079913
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.21784589
## NRO5.1 -0.19109949
## NRO6.1 -0.72411467
## NRA1.1 0.31361784
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.56338433
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.02123485
## NRC2.1 -0.69377329
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 0.48599318
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.42876856
## NRN1.1 0.51842577
## NRN2.1 0.08303491
## NRN3.1 -0.28564876
## NRN4.1 -0.17481299
## NRN5.1 .
## NRN6.1 -0.05596100
## NRE1.1 .
## NRE2.1 -0.88808710
## NRE3.1 0.63365898
## NRE4.1 -0.51211076
## NRE5.1 .
## NRE6.1 -0.48160735
## NRO1.1 .
## NRO2.1 -0.03591601
## NRO3.1 -0.29099264
## NRO4.1 -0.51302391
## NRO5.1 0.02941873
## NRO6.1 .
## NRA1.1 -1.36651366
## NRA2.1 .
## NRA3.1 0.38261074
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.91877665
## NRC1.1 .
## NRC2.1 0.94249519
## NRC3.1 .
## NRC4.1 0.56997213
## NRC5.1 .
## NRC6.1 .
## hamd1 1.99073771
## running cv # 4
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.367647
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.1894515
## NRN1.1 .
## NRN2.1 0.1495208
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.2471408
## NRE3.1 0.1574616
## NRE4.1 -0.4595520
## NRE5.1 .
## NRE6.1 -0.6173584
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.1979240
## NRO5.1 .
## NRO6.1 -0.2475676
## NRA1.1 -0.1818119
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.1079581
## NRA6.1 0.4498184
## NRC1.1 .
## NRC2.1 0.9397815
## NRC3.1 .
## NRC4.1 0.5362859
## NRC5.1 .
## NRC6.1 .
## hamd1 1.3808721
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.25388781
## NRN1.1 0.13981162
## NRN2.1 .
## NRN3.1 0.05841786
## NRN4.1 0.37016215
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.87157075
## NRE2.1 0.31302835
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.34301672
## NRA1.1 0.08204272
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.43690829
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.30033139
## NRC2.1 -0.46094296
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.66201946
## hamd1 0.42572906
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.72723960
## NRN1.1 .
## NRN2.1 0.07730718
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.07292474
## NRE2.1 -0.43784189
## NRE3.1 .
## NRE4.1 -0.07302359
## NRE5.1 .
## NRE6.1 -0.58024317
## NRO1.1 .
## NRO2.1 -0.06170306
## NRO3.1 -0.14792458
## NRO4.1 -0.13683376
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.80604528
## NRA2.1 .
## NRA3.1 0.28827767
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.65129452
## NRC1.1 .
## NRC2.1 0.56581514
## NRC3.1 .
## NRC4.1 0.25139226
## NRC5.1 .
## NRC6.1 .
## hamd1 1.96208396
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.22494371
## NRN1.1 0.17657017
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.84076972
## NRE3.1 0.22261821
## NRE4.1 -0.08555414
## NRE5.1 .
## NRE6.1 -0.33127615
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.30771561
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.62986577
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.02151300
## NRC1.1 .
## NRC2.1 1.03669705
## NRC3.1 .
## NRC4.1 0.54265648
## NRC5.1 .
## NRC6.1 .
## hamd1 2.01312759
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.7713485
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 0.3474595
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.20339093
## NRN1.1 0.70633230
## NRN2.1 0.58237378
## NRN3.1 -0.25661462
## NRN4.1 -0.39186939
## NRN5.1 0.11810506
## NRN6.1 -0.68444586
## NRE1.1 -0.30352068
## NRE2.1 -0.59548453
## NRE3.1 0.97797920
## NRE4.1 -0.49345829
## NRE5.1 -0.04215691
## NRE6.1 -0.83962434
## NRO1.1 -0.01890566
## NRO2.1 -0.56887512
## NRO3.1 -0.02667055
## NRO4.1 -0.53761451
## NRO5.1 0.33706484
## NRO6.1 -0.17902629
## NRA1.1 -1.10465650
## NRA2.1 -0.58817580
## NRA3.1 0.40501877
## NRA4.1 0.46219118
## NRA5.1 0.36999177
## NRA6.1 1.32579716
## NRC1.1 -0.33592278
## NRC2.1 1.33455889
## NRC3.1 0.01854387
## NRC4.1 0.47349356
## NRC5.1 -0.11387932
## NRC6.1 0.05893446
## hamd1 1.88755390
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.38623671
## NRN1.1 0.33268330
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.06839707
## NRE2.1 -0.80700177
## NRE3.1 0.28358343
## NRE4.1 -0.31730380
## NRE5.1 .
## NRE6.1 -0.54041743
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.59470470
## NRO5.1 0.14042014
## NRO6.1 .
## NRA1.1 -0.91301308
## NRA2.1 .
## NRA3.1 0.81774083
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.16033053
## NRC1.1 .
## NRC2.1 0.87690665
## NRC3.1 .
## NRC4.1 0.10763280
## NRC5.1 .
## NRC6.1 .
## hamd1 1.79787647
## running cv # 5
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.970149
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.62196909
## NRN1.1 0.23777088
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.08172951
## NRE2.1 -0.37943988
## NRE3.1 .
## NRE4.1 -0.23287914
## NRE5.1 .
## NRE6.1 -0.18974916
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.10833580
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.38207286
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.48462341
## NRC1.1 .
## NRC2.1 0.84062212
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 1.52886847
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.313433
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.6366579634
## NRN1.1 0.7123382610
## NRN2.1 0.1298828699
## NRN3.1 -0.4526019262
## NRN4.1 -0.3097630708
## NRN5.1 0.2386522470
## NRN6.1 -0.2516742361
## NRE1.1 -0.1810552071
## NRE2.1 -1.0626590377
## NRE3.1 0.6938645788
## NRE4.1 -0.7354201690
## NRE5.1 -0.0497094497
## NRE6.1 -0.6864538540
## NRO1.1 0.1919858027
## NRO2.1 -0.3791259851
## NRO3.1 -0.5115527817
## NRO4.1 -0.3320687900
## NRO5.1 0.3374940243
## NRO6.1 -0.4045168014
## NRA1.1 -1.0909616902
## NRA2.1 -0.6092833852
## NRA3.1 0.9572266570
## NRA4.1 0.6095302393
## NRA5.1 -0.0236532284
## NRA6.1 1.4357163085
## NRC1.1 -0.2453661675
## NRC2.1 1.1032542162
## NRC3.1 0.0007470174
## NRC4.1 0.7311510917
## NRC5.1 -0.2230332801
## NRC6.1 0.1312605270
## hamd1 2.1031629255
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.804525457
## NRN1.1 0.128403983
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.167848772
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.123904066
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.525835807
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.004231968
## hamd1 0.325596921
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.0506684
## NRN1.1 0.2650387
## NRN2.1 0.3421041
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.3237603
## NRE3.1 .
## NRE4.1 -0.1562858
## NRE5.1 .
## NRE6.1 -0.5302036
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.3065816
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.5108854
## NRA2.1 .
## NRA3.1 0.1116352
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.4421128
## NRC1.1 .
## NRC2.1 0.6457125
## NRC3.1 .
## NRC4.1 0.5755186
## NRC5.1 .
## NRC6.1 .
## hamd1 1.6328001
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.10223491
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.17062957
## NRE3.1 0.62692533
## NRE4.1 -0.01819337
## NRE5.1 .
## NRE6.1 -1.12419297
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.19377738
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.18959356
## NRA2.1 .
## NRA3.1 0.04682034
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.94285524
## NRC1.1 .
## NRC2.1 1.11144880
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.03612705
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.2890054
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.6468038
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.5971628
## NRO1.1 -0.1312749
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.2654592
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.6606187
## NRA2.1 .
## NRA3.1 0.1573428
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.2759506
## NRC1.1 .
## NRC2.1 1.0119047
## NRC3.1 .
## NRC4.1 0.4936797
## NRC5.1 .
## NRC6.1 .
## hamd1 1.6708950
## running cv # 6
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.405881533
## NRN1.1 0.738151514
## NRN2.1 0.255305246
## NRN3.1 -0.297529608
## NRN4.1 -0.620131801
## NRN5.1 -0.051340828
## NRN6.1 -0.598525227
## NRE1.1 -0.716955998
## NRE2.1 -1.182089616
## NRE3.1 0.875106574
## NRE4.1 -0.886358159
## NRE5.1 0.224174628
## NRE6.1 -0.879102737
## NRO1.1 0.080784087
## NRO2.1 -0.333156250
## NRO3.1 -0.166287754
## NRO4.1 -0.006052819
## NRO5.1 0.416988309
## NRO6.1 -0.564528541
## NRA1.1 -0.799108751
## NRA2.1 -0.004754158
## NRA3.1 0.802539996
## NRA4.1 0.324645274
## NRA5.1 0.161277995
## NRA6.1 1.065095313
## NRC1.1 -0.325017146
## NRC2.1 1.038667190
## NRC3.1 -0.159847188
## NRC4.1 0.707644174
## NRC5.1 -0.063765447
## NRC6.1 -0.195644222
## hamd1 1.796362589
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.764706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.15830713
## NRN1.1 0.56830513
## NRN2.1 0.42091705
## NRN3.1 0.05559813
## NRN4.1 -0.13136978
## NRN5.1 0.40298075
## NRN6.1 -0.47299427
## NRE1.1 -0.38824810
## NRE2.1 -1.08980497
## NRE3.1 0.98600667
## NRE4.1 -0.56553626
## NRE5.1 0.55156529
## NRE6.1 -0.74364610
## NRO1.1 -0.35315632
## NRO2.1 -0.38656686
## NRO3.1 -0.03110152
## NRO4.1 -0.24749677
## NRO5.1 0.32456272
## NRO6.1 -0.41091013
## NRA1.1 -1.30405091
## NRA2.1 -0.24105242
## NRA3.1 0.76925446
## NRA4.1 0.37400827
## NRA5.1 0.25208435
## NRA6.1 0.97100292
## NRC1.1 -0.11977301
## NRC2.1 1.11242411
## NRC3.1 -0.28810532
## NRC4.1 0.58927015
## NRC5.1 -0.12597667
## NRC6.1 -0.14633295
## hamd1 1.88969850
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.880597
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.190094736
## NRN1.1 .
## NRN2.1 0.098201110
## NRN3.1 -0.182240531
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.102189217
## NRE3.1 0.393575799
## NRE4.1 -0.618668598
## NRE5.1 .
## NRE6.1 -0.457788334
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -1.322710537
## NRO5.1 .
## NRO6.1 -0.005626116
## NRA1.1 -0.869411121
## NRA2.1 .
## NRA3.1 0.319592768
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.181459296
## NRC1.1 .
## NRC2.1 0.909424751
## NRC3.1 .
## NRC4.1 0.529553193
## NRC5.1 .
## NRC6.1 0.023144815
## hamd1 1.506522397
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.87383344
## NRN1.1 0.50532770
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.12218674
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.25621176
## NRE3.1 0.58384979
## NRE4.1 -0.20055325
## NRE5.1 .
## NRE6.1 -0.84828277
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.42063501
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.72080472
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.04011854
## NRA5.1 0.02271093
## NRA6.1 0.93114978
## NRC1.1 .
## NRC2.1 1.26586165
## NRC3.1 .
## NRC4.1 0.31216599
## NRC5.1 .
## NRC6.1 .
## hamd1 1.96546784
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.10404641
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.52593590
## NRE3.1 .
## NRE4.1 .
## NRE5.1 -0.27284408
## NRE6.1 -0.46054740
## NRO1.1 .
## NRO2.1 .
## NRO3.1 -0.04768307
## NRO4.1 -0.23890015
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.72003398
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.60831460
## NRC1.1 .
## NRC2.1 1.32501227
## NRC3.1 .
## NRC4.1 0.31885421
## NRC5.1 .
## NRC6.1 .
## hamd1 2.63003615
## running cv # 7
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.179104
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.24768681
## NRN1.1 0.66475548
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.15397407
## NRE1.1 -0.01918490
## NRE2.1 -1.05212211
## NRE3.1 0.50899559
## NRE4.1 -0.34686210
## NRE5.1 .
## NRE6.1 -0.92397738
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.09871366
## NRO5.1 0.10426358
## NRO6.1 -0.31907602
## NRA1.1 -0.73629049
## NRA2.1 .
## NRA3.1 0.15665623
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.86529279
## NRC1.1 .
## NRC2.1 0.43816239
## NRC3.1 .
## NRC4.1 0.55137725
## NRC5.1 .
## NRC6.1 .
## hamd1 1.84647603
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.37160394
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.07587509
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.20000985
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.81206017
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.37333776
## NRC1.1 -0.22159499
## NRC2.1 -1.48632500
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.51801484
## hamd1 0.33327237
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.24631700
## NRN1.1 0.51492164
## NRN2.1 0.51125080
## NRN3.1 0.41302248
## NRN4.1 -0.37727691
## NRN5.1 -0.06044459
## NRN6.1 -0.47081169
## NRE1.1 -0.16724681
## NRE2.1 -0.61513929
## NRE3.1 0.82942340
## NRE4.1 -0.87552425
## NRE5.1 0.14618026
## NRE6.1 -0.49035745
## NRO1.1 -0.16481743
## NRO2.1 -0.57367489
## NRO3.1 -0.61571276
## NRO4.1 -0.20947382
## NRO5.1 0.04760230
## NRO6.1 -0.43835154
## NRA1.1 -0.88991367
## NRA2.1 -0.33873934
## NRA3.1 0.83962681
## NRA4.1 0.02415892
## NRA5.1 0.58703854
## NRA6.1 1.48558314
## NRC1.1 -0.64084570
## NRC2.1 1.17459523
## NRC3.1 -0.25254933
## NRC4.1 0.91857657
## NRC5.1 -0.13312850
## NRC6.1 0.01470124
## hamd1 1.73854987
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.279412
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.47679461
## NRN1.1 0.06142856
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.29514598
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.45112296
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.05691166
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.68111424
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.58375429
## NRC1.1 .
## NRC2.1 1.02758953
## NRC3.1 .
## NRC4.1 0.08151063
## NRC5.1 .
## NRC6.1 .
## hamd1 1.92758135
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.089552
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.34367117
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.33414126
## NRN5.1 .
## NRN6.1 -0.02931836
## NRE1.1 .
## NRE2.1 -0.46144101
## NRE3.1 0.66661161
## NRE4.1 -0.09206281
## NRE5.1 .
## NRE6.1 -0.98461515
## NRO1.1 .
## NRO2.1 -0.34266513
## NRO3.1 .
## NRO4.1 -0.54255453
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.84462032
## NRA2.1 .
## NRA3.1 0.43376095
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.65412961
## NRC1.1 .
## NRC2.1 1.23335032
## NRC3.1 .
## NRC4.1 0.33835308
## NRC5.1 .
## NRC6.1 0.05172813
## hamd1 1.65661426
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.4156218
## NRN1.1 0.3780111
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -1.0513756
## NRE3.1 0.5329722
## NRE4.1 -0.3404540
## NRE5.1 .
## NRE6.1 -0.1485129
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.5727578
## NRO5.1 0.3017648
## NRO6.1 .
## NRA1.1 -0.9500403
## NRA2.1 .
## NRA3.1 0.0818847
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.7671997
## NRC1.1 .
## NRC2.1 1.5152021
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.3297971
## running cv # 8
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.161765
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.39431033
## NRN1.1 0.16528884
## NRN2.1 0.52730000
## NRN3.1 .
## NRN4.1 -0.06947028
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.68016558
## NRE3.1 0.72493481
## NRE4.1 -0.46310692
## NRE5.1 .
## NRE6.1 -0.65445012
## NRO1.1 .
## NRO2.1 -0.02968198
## NRO3.1 -0.10024379
## NRO4.1 -0.71654029
## NRO5.1 .
## NRO6.1 -0.13225165
## NRA1.1 -0.95273366
## NRA2.1 .
## NRA3.1 0.53671930
## NRA4.1 0.10535422
## NRA5.1 .
## NRA6.1 1.24327138
## NRC1.1 .
## NRC2.1 0.97382695
## NRC3.1 .
## NRC4.1 0.31465166
## NRC5.1 .
## NRC6.1 .
## hamd1 1.65502171
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.104478
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.82404963
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.59039765
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.45597533
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.05828871
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.60714478
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.15146257
## NRC1.1 .
## NRC2.1 0.83719374
## NRC3.1 .
## NRC4.1 0.13965256
## NRC5.1 .
## NRC6.1 .
## hamd1 1.95499726
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.074627
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.23964842
## NRN1.1 .
## NRN2.1 0.25988640
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.46568700
## NRE3.1 0.74597815
## NRE4.1 -0.53370141
## NRE5.1 0.05169238
## NRE6.1 -0.71959063
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.34535870
## NRO5.1 .
## NRO6.1 -0.25505064
## NRA1.1 -0.59754574
## NRA2.1 .
## NRA3.1 0.13214072
## NRA4.1 .
## NRA5.1 0.35696486
## NRA6.1 0.71361341
## NRC1.1 .
## NRC2.1 1.07844999
## NRC3.1 0.06477688
## NRC4.1 0.58475794
## NRC5.1 .
## NRC6.1 .
## hamd1 1.80307350
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.46537815
## NRN1.1 0.07806036
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.01135923
## NRN5.1 .
## NRN6.1 -0.19103531
## NRE1.1 .
## NRE2.1 -0.75105376
## NRE3.1 0.48293702
## NRE4.1 -0.84818821
## NRE5.1 .
## NRE6.1 -0.43492953
## NRO1.1 -0.05456821
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.13695913
## NRA1.1 -1.14983576
## NRA2.1 .
## NRA3.1 0.04300235
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.74445069
## NRC1.1 .
## NRC2.1 1.04876097
## NRC3.1 .
## NRC4.1 0.09014661
## NRC5.1 .
## NRC6.1 .
## hamd1 2.42770813
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.223881
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.8804320
## NRN1.1 0.5126389
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.2541624
## NRE2.1 -0.6883647
## NRE3.1 0.5166097
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.6749268
## NRO1.1 .
## NRO2.1 .
## NRO3.1 -0.1894663
## NRO4.1 -0.5060609
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.6134281
## NRA2.1 .
## NRA3.1 0.5530614
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.0975600
## NRC1.1 .
## NRC2.1 0.9214812
## NRC3.1 .
## NRC4.1 0.3725156
## NRC5.1 .
## NRC6.1 .
## hamd1 1.6749246
## running cv # 9
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.865672
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.10128238
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.01733722
## NRE1.1 .
## NRE2.1 -0.42963030
## NRE3.1 0.62859914
## NRE4.1 -0.66331495
## NRE5.1 .
## NRE6.1 -1.04757499
## NRO1.1 .
## NRO2.1 -0.38541047
## NRO3.1 -0.00450928
## NRO4.1 -0.26033886
## NRO5.1 0.24773574
## NRO6.1 -0.15400084
## NRA1.1 -0.92239926
## NRA2.1 .
## NRA3.1 0.62796832
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.59475264
## NRC1.1 .
## NRC2.1 1.19829552
## NRC3.1 .
## NRC4.1 0.52253297
## NRC5.1 .
## NRC6.1 0.12315520
## hamd1 1.69019889
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.24121067
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 -0.05061968
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 0.56362376
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.12801554
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.56671665
## NRE3.1 0.13400003
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.55978492
## NRO1.1 -0.07405519
## NRO2.1 -0.01569039
## NRO3.1 .
## NRO4.1 -0.64624034
## NRO5.1 .
## NRO6.1 -0.19443038
## NRA1.1 -0.52084224
## NRA2.1 .
## NRA3.1 0.23133590
## NRA4.1 .
## NRA5.1 0.37594435
## NRA6.1 0.83834562
## NRC1.1 .
## NRC2.1 0.66160433
## NRC3.1 .
## NRC4.1 0.11381175
## NRC5.1 .
## NRC6.1 .
## hamd1 1.91354271
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.264706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.69955378
## NRN1.1 0.35526605
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.65376526
## NRE3.1 0.44925159
## NRE4.1 -0.35894244
## NRE5.1 .
## NRE6.1 -0.45407356
## NRO1.1 .
## NRO2.1 -0.16935472
## NRO3.1 .
## NRO4.1 -0.55394523
## NRO5.1 0.19740499
## NRO6.1 -0.25736829
## NRA1.1 -0.68703973
## NRA2.1 .
## NRA3.1 0.09555363
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.01676192
## NRC1.1 .
## NRC2.1 1.18401996
## NRC3.1 .
## NRC4.1 0.56884618
## NRC5.1 .
## NRC6.1 .
## hamd1 1.63296524
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.029851
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.56130680
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.47248691
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.07302614
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.74128891
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 0.31544792
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.30763125
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.14354432
## NRN1.1 0.39055269
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.01076076
## NRE1.1 .
## NRE2.1 -0.76813841
## NRE3.1 0.34972410
## NRE4.1 -0.48674411
## NRE5.1 .
## NRE6.1 -0.38081758
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.06865092
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.77244202
## NRA2.1 .
## NRA3.1 0.07028776
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.91471266
## NRC1.1 .
## NRC2.1 1.22954737
## NRC3.1 .
## NRC4.1 0.09624328
## NRC5.1 .
## NRC6.1 .
## hamd1 1.80238400
## running cv # 10
## running fold # 1
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.552239
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.5252385
## NRN1.1 0.3765571
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.4411178
## NRE3.1 0.4823164
## NRE4.1 -0.8403824
## NRE5.1 .
## NRE6.1 -0.5847252
## NRO1.1 .
## NRO2.1 -0.0141648
## NRO3.1 .
## NRO4.1 -0.5011407
## NRO5.1 .
## NRO6.1 -0.2709826
## NRA1.1 -0.6227153
## NRA2.1 .
## NRA3.1 0.2535165
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.7311941
## NRC1.1 .
## NRC2.1 0.8576563
## NRC3.1 .
## NRC4.1 0.6201735
## NRC5.1 .
## NRC6.1 .
## hamd1 1.6834594
## running fold # 2
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.92063450
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.01606627
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 1.22590661
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.5228298
## NRN1.1 0.8262552
## NRN2.1 0.3532762
## NRN3.1 0.2409356
## NRN4.1 -0.5491963
## NRN5.1 0.3989351
## NRN6.1 -0.4859324
## NRE1.1 -0.6084770
## NRE2.1 -0.4784258
## NRE3.1 0.8818857
## NRE4.1 -0.4537318
## NRE5.1 0.3050848
## NRE6.1 -0.4741910
## NRO1.1 -0.2084126
## NRO2.1 -0.4178712
## NRO3.1 -0.3576059
## NRO4.1 -0.6453201
## NRO5.1 0.5237610
## NRO6.1 -0.5430462
## NRA1.1 -1.1591094
## NRA2.1 -0.2631150
## NRA3.1 0.9145416
## NRA4.1 0.7701583
## NRA5.1 0.5938966
## NRA6.1 1.0796656
## NRC1.1 -0.2693389
## NRC2.1 0.8244951
## NRC3.1 -0.1299550
## NRC4.1 0.6356621
## NRC5.1 0.1953360
## NRC6.1 -0.2053787
## hamd1 1.9775568
## running fold # 3
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.686567
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.58119882
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.22724215
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.99360641
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.37497818
## NRO1.1 .
## NRO2.1 -0.13746288
## NRO3.1 .
## NRO4.1 -0.35165801
## NRO5.1 .
## NRO6.1 -0.01474100
## NRA1.1 -0.65176364
## NRA2.1 .
## NRA3.1 0.06783508
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.19077136
## NRC1.1 .
## NRC2.1 1.75916039
## NRC3.1 .
## NRC4.1 0.59381449
## NRC5.1 .
## NRC6.1 .
## hamd1 1.86393258
## running fold # 4
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.850746
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.76592186
## NRN1.1 0.09246800
## NRN2.1 0.17029144
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.57676545
## NRE3.1 0.35864507
## NRE4.1 -0.05460466
## NRE5.1 .
## NRE6.1 -0.76343961
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.26057302
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.05996753
## NRA2.1 .
## NRA3.1 0.56932504
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.20648373
## NRC1.1 .
## NRC2.1 0.70412584
## NRC3.1 .
## NRC4.1 0.09673093
## NRC5.1 -0.01362919
## NRC6.1 .
## hamd1 1.52839310
## running fold # 5
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.38806
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## 32 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.32985786
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.09088725
## NRE1.1 .
## NRE2.1 -0.74559344
## NRE3.1 0.41930850
## NRE4.1 -0.43741666
## NRE5.1 .
## NRE6.1 -0.88788765
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.68150778
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.95557415
## NRC1.1 .
## NRC2.1 1.04755203
## NRC3.1 .
## NRC4.1 0.06700326
## NRC5.1 .
## NRC6.1 .
## hamd1 2.34309026mult.pred.m4.hd = as.data.frame(mult.pred.m4.hd)
mult.pred.m4.hd$mean = rowMeans(cbind(mult.pred.m4.hd$V1, mult.pred.m4.hd$V2, mult.pred.m4.hd$V3, mult.pred.m4.hd$V4, mult.pred.m4.hd$V5,
mult.pred.m4.hd$V6, mult.pred.m4.hd$V7, mult.pred.m4.hd$V8, mult.pred.m4.hd$V9, mult.pred.m4.hd$V10))
mult.pred.m4.hd = cbind(mult.pred.m4.hd, hd.pai)
mult.pred.m4.hd$indication = if_else(mult.pred.m4.hd$mean < 0, 0, 1)
cbt.indicated.m4.hd = filter(mult.pred.m4.hd, indication == 0)
med.indicated.m4.hd = filter(mult.pred.m4.hd, indication == 1)
# Analyses across tx group
mult.pred.m4.hd$match = if_else(mult.pred.m4.hd$indication==mult.pred.m4.hd$txgrp, 0, 1)
describeBy(mult.pred.m4.hd$y, group = mult.pred.m4.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 127 6.88 5.9 5 6.24 5.93 0 26 26 0.89 0.14 0.52
## Q0.25 Q0.75
## 1 2 11
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 198 10.06 7.93 9 9.49 8.9 0 33 33 0.51 -0.74 0.56
## Q0.25 Q0.75
## 1 3 17t.test(mult.pred.m4.hd$y ~ mult.pred.m4.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m4.hd$y by mult.pred.m4.hd$match
## t = -4.1301, df = 315.89, p-value = 4.647e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.692990 -1.664443
## sample estimates:
## mean in group 0 mean in group 1
## 6.88189 10.06061cohen.d(mult.pred.m4.hd$y ~ mult.pred.m4.hd$match)##
## Cohen's d
##
## d estimate: -0.4408546 (small)
## 95 percent confidence interval:
## lower upper
## -0.6670855 -0.2146236#### Model 5 (Every IV)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(hd.pai)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(hd.pai$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(hd.pai$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = hd.pai[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:34)]
X_train_cbt_enfunc = X_train_cbt_data[,c(1:33)]
y_train_cbt_enfunc = select(X_train_cbt_data, y)
X_test_cbt = hd.pai[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:33)]
X_train_med_data = hd.pai[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:34)]
X_train_med_enfunc = X_train_med_data[,c(1:33)]
y_train_med_enfunc = select(X_train_med_data, y)
X_test_med = hd.pai[med_indexnums_test,]
X_test_med = X_test_med[,c(1:33)]
hd.cbt.train.model = elastic.net.pai(data = X_train_cbt_data, zscored.ivs = X_train_cbt_enfunc,
outcome = y_train_cbt_enfunc)
hd.med.train.model = elastic.net.pai(data = X_train_med_data, zscored.ivs = X_train_med_enfunc,
outcome = y_train_med_enfunc)
cbtpred_cbtgrp = predict.glmnet(hd.cbt.train.model, newx = data.matrix(X_test_cbt))
cbtpred_cbtgrp = cbtpred_cbtgrp[,1]
medpred_medgrp = predict.glmnet(hd.med.train.model, newx = data.matrix(X_test_med))
medpred_medgrp = medpred_medgrp[,1]
cbtpred_medgrp = predict.glmnet(hd.cbt.model5, newx = data.matrix(X_test_med))
cbtpred_medgrp = cbtpred_medgrp[,1]
medpred_cbtgrp = predict.glmnet(hd.med.model5, newx = data.matrix(X_test_cbt))
medpred_cbtgrp = medpred_cbtgrp[,1]
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.m5.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.656716
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.85926092
## NRN1.1 .
## NRN2.1 0.27931059
## NRN3.1 .
## NRN4.1 -0.13409931
## NRN5.1 .
## NRN6.1 -0.10889609
## NRE1.1 .
## NRE2.1 -0.81791598
## NRE3.1 0.50729293
## NRE4.1 -0.48649145
## NRE5.1 0.16970546
## NRE6.1 -1.11634163
## NRO1.1 -0.15211195
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.36626093
## NRO5.1 .
## NRO6.1 -0.04285577
## NRA1.1 -1.10042772
## NRA2.1 .
## NRA3.1 0.08341547
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.85107927
## NRC1.1 .
## NRC2.1 0.66155966
## NRC3.1 .
## NRC4.1 0.59553187
## NRC5.1 .
## NRC6.1 .
## hamd1 1.61480482
## sex 1.02697919
## age 0.29552736
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.691176
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 4.572880908
## NRN1.1 0.608897444
## NRN2.1 0.558064670
## NRN3.1 -0.122444887
## NRN4.1 -0.342113857
## NRN5.1 0.070243984
## NRN6.1 -0.895572044
## NRE1.1 -0.619451344
## NRE2.1 -0.836378325
## NRE3.1 0.770848041
## NRE4.1 -0.550517063
## NRE5.1 -0.228603578
## NRE6.1 -0.408648269
## NRO1.1 0.369772641
## NRO2.1 -0.268548371
## NRO3.1 -0.177119155
## NRO4.1 -0.393557099
## NRO5.1 0.160960804
## NRO6.1 -0.790166307
## NRA1.1 -1.188833140
## NRA2.1 -0.409143511
## NRA3.1 0.941868144
## NRA4.1 0.452290009
## NRA5.1 0.251000937
## NRA6.1 0.854018714
## NRC1.1 -0.586729676
## NRC2.1 1.045076303
## NRC3.1 0.006416137
## NRC4.1 0.957805226
## NRC5.1 -0.049276622
## NRC6.1 -0.049414340
## hamd1 1.913616848
## sex 2.688126683
## age 0.633724954
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.343284
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.27861385
## NRN1.1 0.23195231
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.01658275
## NRE1.1 -0.46112222
## NRE2.1 -0.60583471
## NRE3.1 0.55406748
## NRE4.1 -0.98791055
## NRE5.1 0.19532735
## NRE6.1 -0.27304832
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.53021903
## NRO5.1 0.29768757
## NRO6.1 .
## NRA1.1 -0.34587998
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.95782856
## NRC1.1 .
## NRC2.1 1.17001636
## NRC3.1 .
## NRC4.1 0.24865290
## NRC5.1 .
## NRC6.1 .
## hamd1 1.77168765
## sex .
## age 0.28791860
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.447761
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.56501050
## NRN1.1 0.54327493
## NRN2.1 .
## NRN3.1 -0.06092197
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.74603697
## NRE3.1 0.86144389
## NRE4.1 -0.04802201
## NRE5.1 .
## NRE6.1 -0.38360095
## NRO1.1 .
## NRO2.1 -0.51920092
## NRO3.1 .
## NRO4.1 -0.88029248
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.79707474
## NRA2.1 -0.05082492
## NRA3.1 0.66381731
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.03148205
## NRC1.1 .
## NRC2.1 0.86906785
## NRC3.1 .
## NRC4.1 0.10507246
## NRC5.1 .
## NRC6.1 .
## hamd1 2.00587323
## sex 0.63166712
## age 0.10050686
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.41791
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.05027162
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.10277275
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.06493102
## NRE2.1 -0.40095652
## NRE3.1 0.61351479
## NRE4.1 -0.35931218
## NRE5.1 .
## NRE6.1 -0.96819951
## NRO1.1 .
## NRO2.1 -0.30402201
## NRO3.1 -0.56215505
## NRO4.1 -0.49148094
## NRO5.1 0.28971891
## NRO6.1 .
## NRA1.1 -1.23324677
## NRA2.1 .
## NRA3.1 0.16087561
## NRA4.1 0.43108854
## NRA5.1 0.14210891
## NRA6.1 1.01049912
## NRC1.1 .
## NRC2.1 1.55650970
## NRC3.1 .
## NRC4.1 0.46575606
## NRC5.1 -0.12266905
## NRC6.1 .
## hamd1 2.24053607
## sex 2.05931593
## age .
## running cv # 2
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.136835787
## NRN1.1 0.255367052
## NRN2.1 .
## NRN3.1 0.067952118
## NRN4.1 0.234828570
## NRN5.1 -0.107011041
## NRN6.1 .
## NRE1.1 0.774826145
## NRE2.1 0.587909402
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.016009329
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.197750660
## NRA1.1 .
## NRA2.1 -0.006461883
## NRA3.1 .
## NRA4.1 0.684823573
## NRA5.1 .
## NRA6.1 0.185821900
## NRC1.1 .
## NRC2.1 -0.656456046
## NRC3.1 .
## NRC4.1 0.216688375
## NRC5.1 -0.344696213
## NRC6.1 0.493782057
## hamd1 0.446871034
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.30891040
## NRN1.1 0.51295262
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.15476602
## NRE1.1 .
## NRE2.1 -0.55582132
## NRE3.1 0.49225817
## NRE4.1 -0.47376183
## NRE5.1 0.25430213
## NRE6.1 -0.64057373
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.42600207
## NRO5.1 .
## NRO6.1 -0.09436364
## NRA1.1 -1.05541654
## NRA2.1 .
## NRA3.1 0.55662835
## NRA4.1 0.03734277
## NRA5.1 .
## NRA6.1 0.89319428
## NRC1.1 .
## NRC2.1 1.18814296
## NRC3.1 .
## NRC4.1 0.04067259
## NRC5.1 .
## NRC6.1 .
## hamd1 1.68497644
## sex 1.53129321
## age 0.12909160
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.462687
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.74392456
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.78564025
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.21585774
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.03411024
## NRO5.1 .
## NRO6.1 -0.10107877
## NRA1.1 -0.26220199
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.50187718
## NRA6.1 0.60886653
## NRC1.1 .
## NRC2.1 0.55496139
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 1.77886117
## sex 0.04528936
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.42853404
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.12707672
## NRE1.1 .
## NRE2.1 -0.08759429
## NRE3.1 .
## NRE4.1 -0.30241488
## NRE5.1 .
## NRE6.1 -0.61858519
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.17357756
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.75539669
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.14386101
## NRC1.1 .
## NRC2.1 0.88029556
## NRC3.1 .
## NRC4.1 0.85293637
## NRC5.1 .
## NRC6.1 .
## hamd1 2.51279389
## sex 0.01152225
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.426471
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.87532911
## NRN1.1 .
## NRN2.1 .
## NRN3.1 -0.03906735
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.35249901
## NRE2.1 -1.01479119
## NRE3.1 0.85993082
## NRE4.1 -0.11290243
## NRE5.1 .
## NRE6.1 -0.59532338
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.67836338
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.85854364
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.80231064
## NRC1.1 .
## NRC2.1 0.86662268
## NRC3.1 .
## NRC4.1 0.35218526
## NRC5.1 .
## NRC6.1 .
## hamd1 1.86736015
## sex 0.88814573
## age 0.58829005
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.36971888
## NRN1.1 0.40541432
## NRN2.1 0.28047983
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.24105462
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.58917333
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.42698114
## NRO5.1 0.07449087
## NRO6.1 .
## NRA1.1 -0.63711438
## NRA2.1 .
## NRA3.1 0.57228796
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.62405630
## NRC1.1 .
## NRC2.1 0.58818851
## NRC3.1 .
## NRC4.1 0.05034581
## NRC5.1 .
## NRC6.1 .
## hamd1 1.35488034
## sex 0.03456213
## age .
## running cv # 3
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.44383669
## NRN1.1 0.35402213
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.13405709
## NRE1.1 -0.45719845
## NRE2.1 -0.83447123
## NRE3.1 0.51697140
## NRE4.1 -0.46143364
## NRE5.1 0.28633048
## NRE6.1 -0.78812105
## NRO1.1 -0.04603771
## NRO2.1 -0.00182540
## NRO3.1 .
## NRO4.1 -0.11689001
## NRO5.1 .
## NRO6.1 -0.64108295
## NRA1.1 -0.45352258
## NRA2.1 .
## NRA3.1 0.06044578
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.42242742
## NRC1.1 .
## NRC2.1 0.78831149
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.20977912
## sex 1.35391497
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.671642
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.00982538
## NRN1.1 0.76070857
## NRN2.1 0.42896173
## NRN3.1 -0.04572564
## NRN4.1 -0.53546027
## NRN5.1 -0.02651421
## NRN6.1 -0.73706276
## NRE1.1 -0.35911256
## NRE2.1 -0.98226775
## NRE3.1 1.16288971
## NRE4.1 -0.63947373
## NRE5.1 0.16673337
## NRE6.1 -0.59278826
## NRO1.1 -0.30562971
## NRO2.1 -0.23050028
## NRO3.1 -0.17867162
## NRO4.1 -0.43139524
## NRO5.1 0.29408973
## NRO6.1 -0.26448336
## NRA1.1 -1.05398758
## NRA2.1 0.02629869
## NRA3.1 0.75558872
## NRA4.1 0.28557484
## NRA5.1 0.40688036
## NRA6.1 1.32390845
## NRC1.1 -0.24718218
## NRC2.1 0.98870341
## NRC3.1 -0.15422771
## NRC4.1 0.86660845
## NRC5.1 -0.04318417
## NRC6.1 -0.40302083
## hamd1 1.68231088
## sex 1.40006357
## age 0.41658795
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.52736182
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.39006020
## NRE3.1 0.46085390
## NRE4.1 -0.28623726
## NRE5.1 .
## NRE6.1 -0.50710870
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.90550456
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.81711484
## NRA2.1 .
## NRA3.1 0.08569085
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.04563225
## NRC1.1 .
## NRC2.1 0.94455180
## NRC3.1 .
## NRC4.1 0.28853726
## NRC5.1 .
## NRC6.1 .
## hamd1 2.04165285
## sex 1.72315876
## age 0.43453991
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.573529
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.16584082
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.23090798
## NRE3.1 .
## NRE4.1 -0.22791439
## NRE5.1 .
## NRE6.1 -0.81677731
## NRO1.1 .
## NRO2.1 -0.03498882
## NRO3.1 .
## NRO4.1 -0.26889277
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.03401803
## NRA2.1 .
## NRA3.1 0.20765310
## NRA4.1 .
## NRA5.1 0.08496317
## NRA6.1 0.75349860
## NRC1.1 .
## NRC2.1 1.40780646
## NRC3.1 .
## NRC4.1 0.58763043
## NRC5.1 .
## NRC6.1 .
## hamd1 1.68229609
## sex 0.61731384
## age 0.02468360
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.90375753
## NRN1.1 .
## NRN2.1 0.22310694
## NRN3.1 0.42837271
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.86239524
## NRE2.1 0.42737876
## NRE3.1 -0.45185430
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.60079913
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 0.21784589
## NRO5.1 -0.19109949
## NRO6.1 -0.72411467
## NRA1.1 0.31361784
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.56338433
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.02123485
## NRC2.1 -0.69377329
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 0.48599318
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.42284159
## NRN1.1 0.39527360
## NRN2.1 .
## NRN3.1 -0.17617221
## NRN4.1 -0.09609631
## NRN5.1 .
## NRN6.1 -0.01323327
## NRE1.1 .
## NRE2.1 -0.77582765
## NRE3.1 0.53158692
## NRE4.1 -0.39576415
## NRE5.1 .
## NRE6.1 -0.44711109
## NRO1.1 .
## NRO2.1 .
## NRO3.1 -0.17068549
## NRO4.1 -0.51039445
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.33302677
## NRA2.1 .
## NRA3.1 0.29555136
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.81695433
## NRC1.1 .
## NRC2.1 0.85508173
## NRC3.1 .
## NRC4.1 0.53619118
## NRC5.1 .
## NRC6.1 .
## hamd1 1.94554972
## sex .
## age 0.24888478
## running cv # 4
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.367647
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.03445465
## NRN1.1 .
## NRN2.1 0.09275017
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.23437914
## NRE3.1 0.08041747
## NRE4.1 -0.37500913
## NRE5.1 .
## NRE6.1 -0.57573881
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.27375026
## NRO5.1 .
## NRO6.1 -0.15899753
## NRA1.1 -0.14117216
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.02830091
## NRA6.1 0.36074773
## NRC1.1 .
## NRC2.1 0.88002077
## NRC3.1 .
## NRC4.1 0.42395603
## NRC5.1 .
## NRC6.1 .
## hamd1 1.32339911
## sex 0.71036236
## age .
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.28070835
## NRN1.1 0.03392264
## NRN2.1 .
## NRN3.1 .
## NRN4.1 0.34376543
## NRN5.1 .
## NRN6.1 .
## NRE1.1 0.96763679
## NRE2.1 0.01447438
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 -0.11616845
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 0.28111108
## NRA5.1 .
## NRA6.1 .
## NRC1.1 -0.12044069
## NRC2.1 -0.17630037
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.32731929
## hamd1 0.29127422
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.73055003
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.33087456
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.51947985
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.67026616
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.42445396
## NRC1.1 .
## NRC2.1 0.39727441
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.09446948
## sex .
## age 0.08043101
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.66817908
## NRN1.1 0.17247133
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.81356283
## NRE3.1 0.39241477
## NRE4.1 -0.19218938
## NRE5.1 .
## NRE6.1 -0.34413296
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.45845956
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.74311792
## NRA2.1 .
## NRA3.1 0.02115937
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.03731687
## NRC1.1 .
## NRC2.1 1.01364327
## NRC3.1 .
## NRC4.1 0.52630416
## NRC5.1 .
## NRC6.1 .
## hamd1 1.90218040
## sex 0.96144676
## age 0.38274676
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.7713485
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 0.3474595
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.72764931
## NRN1.1 0.65882161
## NRN2.1 0.49221401
## NRN3.1 -0.19859938
## NRN4.1 -0.33366601
## NRN5.1 -0.01416807
## NRN6.1 -0.72761572
## NRE1.1 -0.40839563
## NRE2.1 -0.55853928
## NRE3.1 1.00353674
## NRE4.1 -0.45525913
## NRE5.1 0.10342821
## NRE6.1 -0.84269351
## NRO1.1 0.04898596
## NRO2.1 -0.61789988
## NRO3.1 -0.08504445
## NRO4.1 -0.64938430
## NRO5.1 0.41967625
## NRO6.1 -0.16734991
## NRA1.1 -1.16527371
## NRA2.1 -0.54741845
## NRA3.1 0.41127939
## NRA4.1 0.39968944
## NRA5.1 0.33297195
## NRA6.1 1.26560441
## NRC1.1 -0.37657405
## NRC2.1 1.32112181
## NRC3.1 -0.03467887
## NRC4.1 0.43832792
## NRC5.1 -0.11315897
## NRC6.1 0.07608306
## hamd1 1.89063143
## sex 1.53172915
## age 0.40527249
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.1166631
## NRN1.1 0.2820605
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.2226153
## NRE2.1 -0.8020136
## NRE3.1 0.3985842
## NRE4.1 -0.3901108
## NRE5.1 .
## NRE6.1 -0.5274632
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.7043058
## NRO5.1 0.2079368
## NRO6.1 .
## NRA1.1 -0.9793697
## NRA2.1 .
## NRA3.1 0.9050961
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.1883732
## NRC1.1 .
## NRC2.1 0.8653384
## NRC3.1 .
## NRC4.1 0.1361416
## NRC5.1 .
## NRC6.1 .
## hamd1 1.8507172
## sex 0.7737141
## age 0.1201475
## running cv # 5
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.970149
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.9685016
## NRN1.1 0.5183013
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.1260478
## NRN5.1 .
## NRN6.1 -0.2358344
## NRE1.1 -0.5005641
## NRE2.1 -0.6103904
## NRE3.1 0.6453823
## NRE4.1 -0.6962778
## NRE5.1 0.3543428
## NRE6.1 -0.2902661
## NRO1.1 .
## NRO2.1 -0.3185262
## NRO3.1 -0.0680546
## NRO4.1 -0.3858759
## NRO5.1 .
## NRO6.1 -0.2147287
## NRA1.1 -0.8506676
## NRA2.1 .
## NRA3.1 0.2284759
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.0774442
## NRC1.1 .
## NRC2.1 1.1336598
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 1.6614293
## sex 1.6585105
## age 0.5700717
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.313433
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.03730905
## NRN1.1 0.62131152
## NRN2.1 0.10342176
## NRN3.1 -0.38625416
## NRN4.1 -0.29494769
## NRN5.1 0.11154002
## NRN6.1 -0.33396519
## NRE1.1 -0.33946414
## NRE2.1 -1.00481427
## NRE3.1 0.69427151
## NRE4.1 -0.61312943
## NRE5.1 0.19322589
## NRE6.1 -0.69288913
## NRO1.1 0.27496489
## NRO2.1 -0.44481231
## NRO3.1 -0.51313397
## NRO4.1 -0.51459779
## NRO5.1 0.49561086
## NRO6.1 -0.33347627
## NRA1.1 -1.20565017
## NRA2.1 -0.60677574
## NRA3.1 0.99492198
## NRA4.1 0.56524649
## NRA5.1 -0.03785662
## NRA6.1 1.37926505
## NRC1.1 -0.34216877
## NRC2.1 1.04552268
## NRC3.1 -0.11268164
## NRC4.1 0.70379868
## NRC5.1 -0.20772134
## NRC6.1 0.17795914
## hamd1 2.10819440
## sex 1.60175790
## age 0.76428104
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.78769951
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.24359248
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 0.05079459
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.14258261
## NRN1.1 0.23820187
## NRN2.1 0.30367099
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.36349348
## NRE3.1 0.11914022
## NRE4.1 -0.19214927
## NRE5.1 .
## NRE6.1 -0.55457415
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.46276812
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.52450537
## NRA2.1 .
## NRA3.1 0.10341904
## NRA4.1 .
## NRA5.1 0.02821723
## NRA6.1 0.45426203
## NRC1.1 .
## NRC2.1 0.64374489
## NRC3.1 .
## NRC4.1 0.55541132
## NRC5.1 .
## NRC6.1 .
## hamd1 1.64904857
## sex 1.18206439
## age .
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.253731
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.10223491
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.17062957
## NRE3.1 0.62692533
## NRE4.1 -0.01819337
## NRE5.1 .
## NRE6.1 -1.12419297
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.19377738
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.18959356
## NRA2.1 .
## NRA3.1 0.04682034
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.94285524
## NRC1.1 .
## NRC2.1 1.11144880
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.03612705
## sex .
## age .
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.238806
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.0762700
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.6289627
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.5887975
## NRO1.1 -0.1179729
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.3364151
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.6208287
## NRA2.1 .
## NRA3.1 0.1158231
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.2596773
## NRC1.1 .
## NRC2.1 0.9880459
## NRC3.1 .
## NRC4.1 0.4749804
## NRC5.1 .
## NRC6.1 .
## hamd1 1.6372170
## sex 0.7569012
## age .
## running cv # 6
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.119403
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.68048875
## NRN1.1 0.72710327
## NRN2.1 0.20603351
## NRN3.1 -0.22273906
## NRN4.1 -0.62544607
## NRN5.1 -0.15269404
## NRN6.1 -0.67855827
## NRE1.1 -0.84609608
## NRE2.1 -1.19985081
## NRE3.1 0.90320182
## NRE4.1 -0.84301682
## NRE5.1 0.33005403
## NRE6.1 -0.88438400
## NRO1.1 0.12809823
## NRO2.1 -0.38274284
## NRO3.1 -0.22882638
## NRO4.1 -0.19459786
## NRO5.1 0.51532739
## NRO6.1 -0.48388141
## NRA1.1 -0.81806852
## NRA2.1 0.03885426
## NRA3.1 0.78628002
## NRA4.1 0.35743462
## NRA5.1 0.06855146
## NRA6.1 0.99358164
## NRC1.1 -0.35650666
## NRC2.1 1.02682523
## NRC3.1 -0.19158243
## NRC4.1 0.66297835
## NRC5.1 -0.11007248
## NRC6.1 -0.18816763
## hamd1 1.79275061
## sex 1.66729565
## age 0.26383423
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.764706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.84213141
## NRN1.1 0.50105494
## NRN2.1 0.34110684
## NRN3.1 0.09357522
## NRN4.1 -0.10644340
## NRN5.1 0.25471344
## NRN6.1 -0.53698638
## NRE1.1 -0.47914878
## NRE2.1 -1.10082665
## NRE3.1 1.00724108
## NRE4.1 -0.52615547
## NRE5.1 0.77288371
## NRE6.1 -0.72268679
## NRO1.1 -0.24606949
## NRO2.1 -0.49048905
## NRO3.1 -0.09368419
## NRO4.1 -0.40832418
## NRO5.1 0.46734865
## NRO6.1 -0.38868695
## NRA1.1 -1.39294850
## NRA2.1 -0.16837681
## NRA3.1 0.79260882
## NRA4.1 0.21868675
## NRA5.1 0.24475159
## NRA6.1 0.95266336
## NRC1.1 -0.22342424
## NRC2.1 1.10959282
## NRC3.1 -0.38864867
## NRC4.1 0.50544819
## NRC5.1 -0.11926504
## NRC6.1 -0.10543566
## hamd1 1.91311734
## sex 2.07363883
## age 0.56239507
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.880597
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.34823582
## NRN1.1 .
## NRN2.1 0.11001260
## NRN3.1 -0.29586703
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.18854291
## NRE3.1 0.63202028
## NRE4.1 -0.67437031
## NRE5.1 .
## NRE6.1 -0.48472601
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -1.46678328
## NRO5.1 .
## NRO6.1 -0.02633629
## NRA1.1 -0.99470957
## NRA2.1 .
## NRA3.1 0.34295070
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.23159818
## NRC1.1 .
## NRC2.1 0.90053340
## NRC3.1 .
## NRC4.1 0.43700501
## NRC5.1 -0.02435918
## NRC6.1 0.07245249
## hamd1 1.46112658
## sex 1.15438211
## age 0.28260375
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 9.24168636
## NRN1.1 0.44880257
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.01117133
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.22550277
## NRE3.1 0.46152629
## NRE4.1 -0.14379638
## NRE5.1 .
## NRE6.1 -0.73045512
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.38978954
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.60843392
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 0.06169895
## NRA6.1 0.76800153
## NRC1.1 .
## NRC2.1 1.12232905
## NRC3.1 .
## NRC4.1 0.27440253
## NRC5.1 .
## NRC6.1 .
## hamd1 1.78934051
## sex 0.38103747
## age 0.08584786
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.56275889
## NRN1.1 0.01467241
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.16885409
## NRE2.1 -0.51241877
## NRE3.1 .
## NRE4.1 -0.01457720
## NRE5.1 -0.19419112
## NRE6.1 -0.39516314
## NRO1.1 .
## NRO2.1 -0.31628912
## NRO3.1 -0.18768618
## NRO4.1 -0.50551671
## NRO5.1 0.29938311
## NRO6.1 .
## NRA1.1 -0.96344678
## NRA2.1 .
## NRA3.1 0.17160106
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.80622821
## NRC1.1 .
## NRC2.1 1.33901341
## NRC3.1 .
## NRC4.1 0.41231948
## NRC5.1 .
## NRC6.1 .
## hamd1 2.50277400
## sex 1.56691414
## age 0.38403258
## running cv # 7
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.179104
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.32247629
## NRN1.1 0.95222921
## NRN2.1 0.11805591
## NRN3.1 -0.21782925
## NRN4.1 -0.12245110
## NRN5.1 -0.13313229
## NRN6.1 -0.57877994
## NRE1.1 -0.61424624
## NRE2.1 -1.07427374
## NRE3.1 0.86959666
## NRE4.1 -0.77515157
## NRE5.1 0.41744603
## NRE6.1 -1.00566762
## NRO1.1 -0.14372227
## NRO2.1 -0.22363476
## NRO3.1 0.18228987
## NRO4.1 -0.33810871
## NRO5.1 0.54727828
## NRO6.1 -0.48868405
## NRA1.1 -0.92175436
## NRA2.1 -0.05649454
## NRA3.1 0.56195664
## NRA4.1 0.28049643
## NRA5.1 0.14874508
## NRA6.1 1.01763886
## NRC1.1 -0.47610148
## NRC2.1 0.68393645
## NRC3.1 0.06440723
## NRC4.1 0.79333866
## NRC5.1 0.06070216
## NRC6.1 -0.27351334
## hamd1 1.79235410
## sex 1.20608257
## age 0.33450800
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.38726719
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.06939271
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.06078960
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 0.63355382
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.34290337
## NRC1.1 -0.07367450
## NRC2.1 -1.35984852
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 0.32557401
## hamd1 0.20647106
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.6550168981
## NRN1.1 0.4770981194
## NRN2.1 0.4831300173
## NRN3.1 0.4396186646
## NRN4.1 -0.3656983812
## NRN5.1 -0.1333391304
## NRN6.1 -0.4693876193
## NRE1.1 -0.2599623388
## NRE2.1 -0.5910435608
## NRE3.1 0.8774177742
## NRE4.1 -0.8626489879
## NRE5.1 0.2144265475
## NRE6.1 -0.4801967202
## NRO1.1 -0.1365304662
## NRO2.1 -0.6048472894
## NRO3.1 -0.6022219717
## NRO4.1 -0.3101931600
## NRO5.1 0.1091004987
## NRO6.1 -0.4404863941
## NRA1.1 -0.9039544374
## NRA2.1 -0.3250629255
## NRA3.1 0.8234609115
## NRA4.1 0.0008291378
## NRA5.1 0.5588574047
## NRA6.1 1.4978019796
## NRC1.1 -0.6719098511
## NRC2.1 1.1662003462
## NRC3.1 -0.2631921303
## NRC4.1 0.8718248469
## NRC5.1 -0.1415772196
## NRC6.1 0.0270395361
## hamd1 1.7310797275
## sex 0.9874243350
## age 0.2039880951
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.279412
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.68067485
## NRN1.1 0.55231395
## NRN2.1 0.46377465
## NRN3.1 -0.37954660
## NRN4.1 -0.09670218
## NRN5.1 -0.13500223
## NRN6.1 -0.53649855
## NRE1.1 -0.37413182
## NRE2.1 -0.44634331
## NRE3.1 0.54705405
## NRE4.1 -0.53646114
## NRE5.1 0.18763302
## NRE6.1 -0.69872707
## NRO1.1 0.41610061
## NRO2.1 -0.16219054
## NRO3.1 -0.40401324
## NRO4.1 -0.65289584
## NRO5.1 0.29919045
## NRO6.1 -0.33102663
## NRA1.1 -1.03834257
## NRA2.1 -0.20865023
## NRA3.1 0.51442600
## NRA4.1 0.40546423
## NRA5.1 0.20116421
## NRA6.1 1.01213761
## NRC1.1 0.03318766
## NRC2.1 1.29586599
## NRC3.1 -0.04148729
## NRC4.1 0.63649983
## NRC5.1 -0.30477100
## NRC6.1 -0.24275807
## hamd1 1.84696469
## sex 1.74586048
## age 0.44192923
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.089552
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.36585460
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.37650643
## NRN5.1 .
## NRN6.1 -0.25498084
## NRE1.1 .
## NRE2.1 -0.53423652
## NRE3.1 0.90867709
## NRE4.1 -0.16973806
## NRE5.1 .
## NRE6.1 -1.10880649
## NRO1.1 .
## NRO2.1 -0.52410618
## NRO3.1 .
## NRO4.1 -0.77229966
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -1.03446076
## NRA2.1 .
## NRA3.1 0.42101081
## NRA4.1 0.12183408
## NRA5.1 .
## NRA6.1 0.75532562
## NRC1.1 .
## NRC2.1 1.32385575
## NRC3.1 .
## NRC4.1 0.33549678
## NRC5.1 .
## NRC6.1 0.01773046
## hamd1 1.71126705
## sex 1.83908313
## age 0.24254237
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.61194
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.28851657
## NRN1.1 0.25186570
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.27410051
## NRE2.1 -0.93701241
## NRE3.1 0.69077290
## NRE4.1 -0.42687916
## NRE5.1 .
## NRE6.1 -0.01508613
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.93352852
## NRO5.1 0.55319507
## NRO6.1 .
## NRA1.1 -1.28458997
## NRA2.1 .
## NRA3.1 0.18644544
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.79493602
## NRC1.1 .
## NRC2.1 1.38196621
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 2.43019831
## sex 1.94064857
## age 0.73068782
## running cv # 8
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.161765
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.088213010
## NRN1.1 0.110903321
## NRN2.1 0.416489401
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.042140369
## NRE2.1 -0.612633472
## NRE3.1 0.739699905
## NRE4.1 -0.439330878
## NRE5.1 .
## NRE6.1 -0.605099378
## NRO1.1 .
## NRO2.1 -0.012733733
## NRO3.1 -0.077060223
## NRO4.1 -0.818773130
## NRO5.1 .
## NRO6.1 -0.041508179
## NRA1.1 -0.988214281
## NRA2.1 .
## NRA3.1 0.507932462
## NRA4.1 0.004369611
## NRA5.1 .
## NRA6.1 1.186326832
## NRC1.1 .
## NRC2.1 0.928616105
## NRC3.1 .
## NRC4.1 0.271636061
## NRC5.1 .
## NRC6.1 .
## hamd1 1.646958089
## sex 0.829471840
## age 0.305570743
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.104478
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.46012374
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.58991019
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.45031270
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.08347292
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.60343550
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.14562590
## NRC1.1 .
## NRC2.1 0.83114907
## NRC3.1 .
## NRC4.1 0.12552713
## NRC5.1 .
## NRC6.1 .
## hamd1 1.95072132
## sex 0.22384398
## age .
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.074627
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.2974110108
## NRN1.1 0.2008744269
## NRN2.1 0.4323555610
## NRN3.1 -0.1514570952
## NRN4.1 0.0004062153
## NRN5.1 -0.0480072616
## NRN6.1 -0.3232004534
## NRE1.1 -0.2890953188
## NRE2.1 -0.6565470498
## NRE3.1 1.1553111960
## NRE4.1 -0.8871579777
## NRE5.1 0.4790540504
## NRE6.1 -0.8717562562
## NRO1.1 0.0208773254
## NRO2.1 -0.2501212636
## NRO3.1 -0.1443200769
## NRO4.1 -0.5775918877
## NRO5.1 0.4217975677
## NRO6.1 -0.4673958702
## NRA1.1 -0.7898469754
## NRA2.1 -0.1713575615
## NRA3.1 0.4920255405
## NRA4.1 0.0171296018
## NRA5.1 0.6694115700
## NRA6.1 0.9288628095
## NRC1.1 -0.1110164882
## NRC2.1 1.2793473124
## NRC3.1 0.2069678900
## NRC4.1 0.7671838176
## NRC5.1 -0.1070082526
## NRC6.1 -0.3604315380
## hamd1 1.7622012704
## sex 1.8131630910
## age 0.4823423944
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.985075
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.56093898
## NRN1.1 0.31555778
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -0.14024152
## NRN5.1 .
## NRN6.1 -0.38549373
## NRE1.1 .
## NRE2.1 -0.77763084
## NRE3.1 0.62711383
## NRE4.1 -0.89038070
## NRE5.1 .
## NRE6.1 -0.44171386
## NRO1.1 -0.11300085
## NRO2.1 -0.18650604
## NRO3.1 .
## NRO4.1 -0.19336055
## NRO5.1 0.13511415
## NRO6.1 -0.15083562
## NRA1.1 -1.30186008
## NRA2.1 .
## NRA3.1 0.07409462
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.77086971
## NRC1.1 .
## NRC2.1 1.02747993
## NRC3.1 .
## NRC4.1 0.03809788
## NRC5.1 .
## NRC6.1 .
## hamd1 2.29209832
## sex 1.17389193
## age 0.41044041
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.223881
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.645618800
## NRN1.1 0.470252276
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 -0.305529686
## NRE2.1 -0.630231792
## NRE3.1 0.442902390
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.641401438
## NRO1.1 .
## NRO2.1 .
## NRO3.1 -0.158334413
## NRO4.1 -0.540665484
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.529788579
## NRA2.1 .
## NRA3.1 0.498178799
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.026316136
## NRC1.1 .
## NRC2.1 0.870235894
## NRC3.1 .
## NRC4.1 0.336107121
## NRC5.1 .
## NRC6.1 .
## hamd1 1.652121671
## sex 0.762900600
## age 0.007925869
## running cv # 9
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.865672
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 5.95679575
## NRN1.1 0.13394770
## NRN2.1 0.42717726
## NRN3.1 0.25411806
## NRN4.1 -0.24242981
## NRN5.1 -0.03837769
## NRN6.1 -0.47587048
## NRE1.1 -0.56526207
## NRE2.1 -0.47487328
## NRE3.1 0.94999177
## NRE4.1 -0.73980242
## NRE5.1 -0.01739474
## NRE6.1 -0.77820222
## NRO1.1 -0.08004953
## NRO2.1 -0.61969174
## NRO3.1 -0.28319536
## NRO4.1 -0.50740192
## NRO5.1 0.77438131
## NRO6.1 -0.45551927
## NRA1.1 -0.92185299
## NRA2.1 0.03118217
## NRA3.1 0.80672878
## NRA4.1 0.14581293
## NRA5.1 0.19042068
## NRA6.1 0.71214668
## NRC1.1 -0.05265646
## NRC2.1 1.18932514
## NRC3.1 -0.05918928
## NRC4.1 0.58643757
## NRC5.1 -0.36380748
## NRC6.1 0.38507964
## hamd1 1.63758618
## sex 1.95836440
## age 0.34508979
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.24121067
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 -0.05061968
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 0.56362376
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.49610351
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.58080609
## NRE3.1 0.17406412
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.56454705
## NRO1.1 -0.07484309
## NRO2.1 -0.03954540
## NRO3.1 .
## NRO4.1 -0.68660167
## NRO5.1 .
## NRO6.1 -0.19682481
## NRA1.1 -0.55721554
## NRA2.1 .
## NRA3.1 0.24441291
## NRA4.1 .
## NRA5.1 0.36365452
## NRA6.1 0.84920074
## NRC1.1 .
## NRC2.1 0.66467079
## NRC3.1 .
## NRC4.1 0.11245962
## NRC5.1 .
## NRC6.1 .
## hamd1 1.91856178
## sex 0.38842014
## age 0.06052124
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.264706
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.70674687
## NRN1.1 0.18236163
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.63226829
## NRE3.1 0.50883880
## NRE4.1 -0.35243466
## NRE5.1 .
## NRE6.1 -0.41636379
## NRO1.1 .
## NRO2.1 -0.14528752
## NRO3.1 .
## NRO4.1 -0.68340115
## NRO5.1 0.20681611
## NRO6.1 -0.17979116
## NRA1.1 -0.94499867
## NRA2.1 .
## NRA3.1 0.05601445
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.12755140
## NRC1.1 .
## NRC2.1 1.22629450
## NRC3.1 .
## NRC4.1 0.54675895
## NRC5.1 .
## NRC6.1 .
## hamd1 1.74731730
## sex 0.59614574
## age 0.47351405
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.029851
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.84442291
## NRN1.1 0.01542815
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.60491338
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.33752142
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.98656238
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.29590839
## NRC1.1 .
## NRC2.1 0.56450044
## NRC3.1 .
## NRC4.1 0.07629666
## NRC5.1 .
## NRC6.1 .
## hamd1 2.33064918
## sex 0.44007259
## age 0.07724223
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.164179
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.83312590
## NRN1.1 0.33825556
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.02427035
## NRE1.1 .
## NRE2.1 -0.76249175
## NRE3.1 0.39209004
## NRE4.1 -0.47025333
## NRE5.1 .
## NRE6.1 -0.40852229
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.18821649
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.73488231
## NRA2.1 .
## NRA3.1 0.03020142
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.91196462
## NRC1.1 .
## NRC2.1 1.21499331
## NRC3.1 .
## NRC4.1 0.05555244
## NRC5.1 .
## NRC6.1 .
## hamd1 1.80769012
## sex 0.82525573
## age .
## running cv # 10
## running fold # 1
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.552239
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.12220242
## NRN1.1 0.31462262
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.44834158
## NRE3.1 0.52884640
## NRE4.1 -0.84034141
## NRE5.1 .
## NRE6.1 -0.58734050
## NRO1.1 .
## NRO2.1 -0.03704338
## NRO3.1 .
## NRO4.1 -0.60866292
## NRO5.1 .
## NRO6.1 -0.23653799
## NRA1.1 -0.62286186
## NRA2.1 .
## NRA3.1 0.23047072
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.73921974
## NRC1.1 .
## NRC2.1 0.83983418
## NRC3.1 .
## NRC4.1 0.58093926
## NRC5.1 .
## NRC6.1 .
## hamd1 1.68050821
## sex 0.85241868
## age 0.05655851
## running fold # 2
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.92063450
## NRN1.1 .
## NRN2.1 .
## NRN3.1 0.01606627
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 1.22590661
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.5577604
## NRN1.1 0.7701044
## NRN2.1 0.3204015
## NRN3.1 0.2792590
## NRN4.1 -0.5405604
## NRN5.1 0.3020861
## NRN6.1 -0.5550726
## NRE1.1 -0.7123956
## NRE2.1 -0.4527344
## NRE3.1 0.9058137
## NRE4.1 -0.3936250
## NRE5.1 0.4525065
## NRE6.1 -0.4948685
## NRO1.1 -0.1252055
## NRO2.1 -0.4377312
## NRO3.1 -0.3519540
## NRO4.1 -0.7430948
## NRO5.1 0.5976207
## NRO6.1 -0.5268442
## NRA1.1 -1.2017265
## NRA2.1 -0.2240850
## NRA3.1 0.9478065
## NRA4.1 0.7400729
## NRA5.1 0.5563671
## NRA6.1 1.0484645
## NRC1.1 -0.3377936
## NRC2.1 0.8205520
## NRC3.1 -0.2111597
## NRC4.1 0.6145809
## NRC5.1 0.1628374
## NRC6.1 -0.1748164
## hamd1 1.9933690
## sex 1.2216697
## age 0.3817744
## running fold # 3
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.686567
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.123350e+00
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 -2.805610e-01
## NRN5.1 .
## NRN6.1 -1.463378e-05
## NRE1.1 .
## NRE2.1 -9.987461e-01
## NRE3.1 7.991107e-02
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -3.582359e-01
## NRO1.1 .
## NRO2.1 -2.087830e-01
## NRO3.1 .
## NRO4.1 -5.403542e-01
## NRO5.1 .
## NRO6.1 -3.365777e-02
## NRA1.1 -7.165091e-01
## NRA2.1 .
## NRA3.1 1.196399e-01
## NRA4.1 .
## NRA5.1 .
## NRA6.1 2.222894e-01
## NRC1.1 .
## NRC2.1 1.658928e+00
## NRC3.1 .
## NRC4.1 5.487680e-01
## NRC5.1 .
## NRC6.1 .
## hamd1 1.775983e+00
## sex 1.509389e+00
## age 9.963273e-02
## running fold # 4
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 6.850746
## NRN1.1 0.000000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 8.49236235
## NRN1.1 0.07556566
## NRN2.1 0.14117291
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 -0.48744634
## NRE3.1 0.23331721
## NRE4.1 .
## NRE5.1 .
## NRE6.1 -0.68624299
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 -0.22222235
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.99290440
## NRA2.1 .
## NRA3.1 0.48907530
## NRA4.1 .
## NRA5.1 .
## NRA6.1 1.11057468
## NRC1.1 .
## NRC2.1 0.65616143
## NRC3.1 .
## NRC4.1 0.03822250
## NRC5.1 .
## NRC6.1 .
## hamd1 1.45767142
## sex 0.16528851
## age 0.13808467
## running fold # 5
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.38806
## NRN1.1 0.00000
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 .
## NRE1.1 .
## NRE2.1 .
## NRE3.1 .
## NRE4.1 .
## NRE5.1 .
## NRE6.1 .
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 .
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 .
## NRC1.1 .
## NRC2.1 .
## NRC3.1 .
## NRC4.1 .
## NRC5.1 .
## NRC6.1 .
## hamd1 .
## sex .
## age .
## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) 7.80661061
## NRN1.1 .
## NRN2.1 .
## NRN3.1 .
## NRN4.1 .
## NRN5.1 .
## NRN6.1 -0.15810454
## NRE1.1 .
## NRE2.1 -0.76889582
## NRE3.1 0.44576300
## NRE4.1 -0.45578588
## NRE5.1 .
## NRE6.1 -0.89725014
## NRO1.1 .
## NRO2.1 .
## NRO3.1 .
## NRO4.1 .
## NRO5.1 .
## NRO6.1 .
## NRA1.1 -0.74967289
## NRA2.1 .
## NRA3.1 .
## NRA4.1 .
## NRA5.1 .
## NRA6.1 0.90765149
## NRC1.1 .
## NRC2.1 0.98844301
## NRC3.1 .
## NRC4.1 0.02171835
## NRC5.1 .
## NRC6.1 .
## hamd1 2.32197758
## sex 0.95975092
## age 0.16402331mult.pred.m5.hd = as.data.frame(mult.pred.m5.hd)
mult.pred.m5.hd$mean = rowMeans(cbind(mult.pred.m5.hd$V1, mult.pred.m5.hd$V2, mult.pred.m5.hd$V3, mult.pred.m5.hd$V4, mult.pred.m5.hd$V5,
mult.pred.m5.hd$V6, mult.pred.m5.hd$V7, mult.pred.m5.hd$V8, mult.pred.m5.hd$V9, mult.pred.m5.hd$V10))
mult.pred.m5.hd = cbind(mult.pred.m5.hd, hd.pai)
mult.pred.m5.hd$indication = if_else(mult.pred.m5.hd$mean < 0, 0, 1)
cbt.indicated.m5.hd = filter(mult.pred.m5.hd, indication == 0)
med.indicated.m5.hd = filter(mult.pred.m5.hd, indication == 1)
# Analyses across tx group
mult.pred.m5.hd$match = if_else(mult.pred.m5.hd$indication==mult.pred.m5.hd$txgrp, 0, 1)
describeBy(mult.pred.m5.hd$y, group = mult.pred.m5.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 124 7.06 5.92 5 6.45 5.93 0 26 26 0.82 0.04 0.53
## Q0.25 Q0.75
## 1 2 11
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 201 9.9 7.95 8 9.29 8.9 0 33 33 0.54 -0.73 0.56
## Q0.25 Q0.75
## 1 3 17t.test(mult.pred.m5.hd$y ~ mult.pred.m5.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.m5.hd$y by mult.pred.m5.hd$match
## t = -3.6701, df = 311.76, p-value = 0.0002851
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.356389 -1.315574
## sample estimates:
## mean in group 0 mean in group 1
## 7.064516 9.900498cohen.d(mult.pred.m5.hd$y ~ mult.pred.m5.hd$match)##
## Cohen's d
##
## d estimate: -0.3914027 (small)
## 95 percent confidence interval:
## lower upper
## -0.6180763 -0.1647290#### Model 6 (Pre-tx HAM-D only)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.hd)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.hd$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.hd$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.hd[cbt_indexnums_train,]
X_train_cbt_data = select(X_train_cbt_data, hamd1, hamd2)
X_train_cbt_data = rename(X_train_cbt_data, y = hamd2)
X_test_cbt = neo.depr.hd[cbt_indexnums_test,]
X_test_cbt = select(X_test_cbt, hamd1, hamd2)
X_test_cbt = rename(X_test_cbt, y = hamd2)
X_train_med_data = neo.depr.hd[med_indexnums_train,]
X_train_med_data = select(X_train_med_data, hamd1, hamd2)
X_train_med_data = rename(X_train_med_data, y = hamd2)
X_test_med = neo.depr.hd[med_indexnums_test,]
X_test_med = select(X_test_med, hamd1, hamd2)
X_test_med = rename(X_test_med, y = hamd2)
hd.cbt.train.model = regression.pai(data = X_train_cbt_data)
hd.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(hd.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(hd.med.train.model, X_test_med)
cbtpred_medgrp = predict(hd.cbt.lm6, X_test_med)
medpred_cbtgrp = predict(hd.med.lm6, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm.hd = as.data.frame(mult.pred.lm.hd)
mult.pred.lm.hd$mean = rowMeans(cbind(mult.pred.lm.hd$V1, mult.pred.lm.hd$V2, mult.pred.lm.hd$V3, mult.pred.lm.hd$V4, mult.pred.lm.hd$V5,
mult.pred.lm.hd$V6, mult.pred.lm.hd$V7, mult.pred.lm.hd$V8, mult.pred.lm.hd$V9, mult.pred.lm.hd$V10))
mult.pred.lm.hd = cbind(mult.pred.lm.hd, neo.depr.hd)
mult.pred.lm.hd$indication = if_else(mult.pred.lm.hd$mean < 0, 0, 1)
cbt.indicated.lm.hd = filter(mult.pred.lm.hd, indication == 0)
med.indicated.lm.hd = filter(mult.pred.lm.hd, indication == 1)
# Analyses across tx group
mult.pred.lm.hd$match = if_else(mult.pred.lm.hd$indication==mult.pred.lm.hd$txgrp, 0, 1)
describeBy(mult.pred.lm.hd$hamd2, group = mult.pred.lm.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 85 7.38 6.3 5 6.72 5.93 0 26 26 0.83 -0.1 0.68
## Q0.25 Q0.75
## 1 2 12
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 240 9.33 7.65 7 8.67 7.41 0 33 33 0.65 -0.51 0.49
## Q0.25 Q0.75
## 1 3 15.25t.test(mult.pred.lm.hd$hamd2 ~ mult.pred.lm.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm.hd$hamd2 by mult.pred.lm.hd$match
## t = -2.3165, df = 177.84, p-value = 0.02167
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.616138 -0.289254
## sample estimates:
## mean in group 0 mean in group 1
## 7.376471 9.329167cohen.d(mult.pred.lm.hd$hamd2 ~ mult.pred.lm.hd$match)##
## Cohen's d
##
## d estimate: -0.2665475 (small)
## 95 percent confidence interval:
## lower upper
## -0.51571409 -0.01738093######### Ordinary least squares versions of the above models ##########
#### Model 1 (Pre-tx HAM-D and demographics)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.hd)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.hd$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.hd$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.hd[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(31, 32, 35, 36)]
X_train_cbt_data = rename(X_train_cbt_data, y = hamd2)
X_test_cbt = neo.depr.hd[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(31, 32, 35, 36)]
X_test_cbt = rename(X_test_cbt, y = hamd2)
X_train_med_data = neo.depr.hd[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(31, 32, 35, 36)]
X_train_med_data = rename(X_train_med_data, y = hamd2)
X_test_med = neo.depr.hd[med_indexnums_test,]
X_test_med = X_test_med[,c(31, 32, 35, 36)]
X_test_med = rename(X_test_med, y = hamd2)
hd.cbt.train.model = regression.pai(data = X_train_cbt_data)
hd.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(hd.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(hd.med.train.model, X_test_med)
cbtpred_medgrp = predict(hd.cbt.lm1, X_test_med)
medpred_cbtgrp = predict(hd.med.lm1, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm1.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm1.hd = as.data.frame(mult.pred.lm1.hd)
mult.pred.lm1.hd$mean = rowMeans(cbind(mult.pred.lm1.hd$V1, mult.pred.lm1.hd$V2, mult.pred.lm1.hd$V3, mult.pred.lm1.hd$V4, mult.pred.lm1.hd$V5,
mult.pred.lm1.hd$V6, mult.pred.lm1.hd$V7, mult.pred.lm1.hd$V8, mult.pred.lm1.hd$V9, mult.pred.lm1.hd$V10))
mult.pred.lm1.hd = cbind(mult.pred.lm1.hd, neo.depr.hd)
mult.pred.lm1.hd$indication = if_else(mult.pred.lm1.hd$mean < 0, 0, 1)
cbt.indicated.lm1.hd = filter(mult.pred.lm1.hd, indication == 0)
med.indicated.lm1.hd = filter(mult.pred.lm1.hd, indication == 1)
# Analyses across tx group
mult.pred.lm1.hd$match = if_else(mult.pred.lm1.hd$indication==mult.pred.lm1.hd$txgrp, 0, 1)
describeBy(mult.pred.lm1.hd$hamd2, group = mult.pred.lm1.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 98 7.84 6.5 6 7.21 5.93 0 26 26 0.78 -0.26 0.66
## Q0.25 Q0.75
## 1 3 12
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 227 9.24 7.68 7 8.57 8.9 0 33 33 0.66 -0.48 0.51
## Q0.25 Q0.75
## 1 3 15t.test(mult.pred.lm1.hd$hamd2 ~ mult.pred.lm1.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm1.hd$hamd2 by mult.pred.lm1.hd$match
## t = -1.6908, df = 215.66, p-value = 0.09233
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.0441087 0.2329966
## sample estimates:
## mean in group 0 mean in group 1
## 7.836735 9.242291cohen.d(mult.pred.lm1.hd$hamd2 ~ mult.pred.lm1.hd$match)##
## Cohen's d
##
## d estimate: -0.1912881 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.42953608 0.04695995#### Model 2 (FFM facets only)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.hd)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.hd$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.hd$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.hd[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 32)]
X_train_cbt_data = rename(X_train_cbt_data, y = hamd2)
X_test_cbt = neo.depr.hd[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30, 32)]
X_test_cbt = rename(X_test_cbt, y = hamd2)
X_train_med_data = neo.depr.hd[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 32)]
X_train_med_data = rename(X_train_med_data, y = hamd2)
X_test_med = neo.depr.hd[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30, 32)]
X_test_med = rename(X_test_med, y = hamd2)
hd.cbt.train.model = regression.pai(data = X_train_cbt_data)
hd.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(hd.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(hd.med.train.model, X_test_med)
cbtpred_medgrp = predict(hd.cbt.lm2, X_test_med)
medpred_cbtgrp = predict(hd.med.lm2, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm2.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm2.hd = as.data.frame(mult.pred.lm2.hd)
mult.pred.lm2.hd$mean = rowMeans(cbind(mult.pred.lm2.hd$V1, mult.pred.lm2.hd$V2, mult.pred.lm2.hd$V3, mult.pred.lm2.hd$V4, mult.pred.lm2.hd$V5,
mult.pred.lm2.hd$V6, mult.pred.lm2.hd$V7, mult.pred.lm2.hd$V8, mult.pred.lm2.hd$V9, mult.pred.lm2.hd$V10))
mult.pred.lm2.hd = cbind(mult.pred.lm2.hd, neo.depr.hd)
mult.pred.lm2.hd$indication = if_else(mult.pred.lm2.hd$mean < 0, 0, 1)
cbt.indicated.lm2.hd = filter(mult.pred.lm2.hd, indication == 0)
med.indicated.lm2.hd = filter(mult.pred.lm2.hd, indication == 1)
# Analyses across tx group
mult.pred.lm2.hd$match = if_else(mult.pred.lm2.hd$indication==mult.pred.lm2.hd$txgrp, 0, 1)
describeBy(mult.pred.lm2.hd$hamd2, group = mult.pred.lm2.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 125 8.1 6.43 7 7.52 5.93 0 26 26 0.63 -0.4 0.57
## Q0.25 Q0.75
## 1 3 12
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 200 9.27 7.88 6 8.51 7.41 0 33 33 0.69 -0.53 0.56
## Q0.25 Q0.75
## 1 3 16t.test(mult.pred.lm2.hd$hamd2 ~ mult.pred.lm2.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm2.hd$hamd2 by mult.pred.lm2.hd$match
## t = -1.4669, df = 300.94, p-value = 0.1435
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.7489607 0.4009607
## sample estimates:
## mean in group 0 mean in group 1
## 8.096 9.270cohen.d(mult.pred.lm2.hd$hamd2 ~ mult.pred.lm2.hd$match)##
## Cohen's d
##
## d estimate: -0.159641 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.38428978 0.06500778#### Model 3 (Demographics and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.hd)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.hd$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.hd$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.hd[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:30, 32, 35, 36)]
X_train_cbt_data = rename(X_train_cbt_data, y = hamd2)
X_test_cbt = neo.depr.hd[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:30, 32, 35, 36)]
X_test_cbt = rename(X_test_cbt, y = hamd2)
X_train_med_data = neo.depr.hd[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:30, 32, 35, 36)]
X_train_med_data = rename(X_train_med_data, y = hamd2)
X_test_med = neo.depr.hd[med_indexnums_test,]
X_test_med = X_test_med[,c(1:30, 32, 35, 36)]
X_test_med = rename(X_test_med, y = hamd2)
hd.cbt.train.model = regression.pai(data = X_train_cbt_data)
hd.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(hd.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(hd.med.train.model, X_test_med)
cbtpred_medgrp = predict(hd.cbt.lm3, X_test_med)
medpred_cbtgrp = predict(hd.med.lm3, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm3.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm3.hd = as.data.frame(mult.pred.lm3.hd)
mult.pred.lm3.hd$mean = rowMeans(cbind(mult.pred.lm3.hd$V1, mult.pred.lm3.hd$V2, mult.pred.lm3.hd$V3, mult.pred.lm3.hd$V4, mult.pred.lm3.hd$V5,
mult.pred.lm3.hd$V6, mult.pred.lm3.hd$V7, mult.pred.lm3.hd$V8, mult.pred.lm3.hd$V9, mult.pred.lm3.hd$V10))
mult.pred.lm3.hd = cbind(mult.pred.lm3.hd, neo.depr.hd)
mult.pred.lm3.hd$indication = if_else(mult.pred.lm3.hd$mean < 0, 0, 1)
cbt.indicated.lm3.hd = filter(mult.pred.lm3.hd, indication == 0)
med.indicated.lm3.hd = filter(mult.pred.lm3.hd, indication == 1)
# Analyses across tx group
mult.pred.lm3.hd$match = if_else(mult.pred.lm3.hd$indication==mult.pred.lm3.hd$txgrp, 0, 1)
describeBy(mult.pred.lm3.hd$hamd2, group = mult.pred.lm3.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 127 8.14 6.43 7 7.61 7.41 0 26 26 0.58 -0.48 0.57
## Q0.25 Q0.75
## 1 3 12
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 198 9.25 7.89 6 8.5 7.41 0 33 33 0.71 -0.52 0.56
## Q0.25 Q0.75
## 1 3 16t.test(mult.pred.lm3.hd$hamd2 ~ mult.pred.lm3.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm3.hd$hamd2 by mult.pred.lm3.hd$match
## t = -1.3887, df = 305.16, p-value = 0.1659
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.684769 0.463183
## sample estimates:
## mean in group 0 mean in group 1
## 8.141732 9.252525cohen.d(mult.pred.lm3.hd$hamd2 ~ mult.pred.lm3.hd$match)##
## Cohen's d
##
## d estimate: -0.1510005 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.37496242 0.07296148#### Model 4 (Pre-tx HAM-D and FFM facets)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.hd)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.hd$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.hd$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.hd[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:32)]
X_train_cbt_data = rename(X_train_cbt_data, y = hamd2)
X_test_cbt = neo.depr.hd[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:32)]
X_test_cbt = rename(X_test_cbt, y = hamd2)
X_train_med_data = neo.depr.hd[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:32)]
X_train_med_data = rename(X_train_med_data, y = hamd2)
X_test_med = neo.depr.hd[med_indexnums_test,]
X_test_med = X_test_med[,c(1:32)]
X_test_med = rename(X_test_med, y = hamd2)
hd.cbt.train.model = regression.pai(data = X_train_cbt_data)
hd.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(hd.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(hd.med.train.model, X_test_med)
cbtpred_medgrp = predict(hd.cbt.lm4, X_test_med)
medpred_cbtgrp = predict(hd.med.lm4, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm4.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm4.hd = as.data.frame(mult.pred.lm4.hd)
mult.pred.lm4.hd$mean = rowMeans(cbind(mult.pred.lm4.hd$V1, mult.pred.lm4.hd$V2, mult.pred.lm4.hd$V3, mult.pred.lm4.hd$V4, mult.pred.lm4.hd$V5,
mult.pred.lm4.hd$V6, mult.pred.lm4.hd$V7, mult.pred.lm4.hd$V8, mult.pred.lm4.hd$V9, mult.pred.lm4.hd$V10))
mult.pred.lm4.hd = cbind(mult.pred.lm4.hd, neo.depr.hd)
mult.pred.lm4.hd$indication = if_else(mult.pred.lm4.hd$mean < 0, 0, 1)
cbt.indicated.lm4.hd = filter(mult.pred.lm4.hd, indication == 0)
med.indicated.lm4.hd = filter(mult.pred.lm4.hd, indication == 1)
# Analyses across tx group
mult.pred.lm4.hd$match = if_else(mult.pred.lm4.hd$indication==mult.pred.lm4.hd$txgrp, 0, 1)
describeBy(mult.pred.lm4.hd$hamd2, group = mult.pred.lm4.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 148 8.11 6.74 7 7.41 6.67 0 33 33 0.88 0.39 0.55
## Q0.25 Q0.75
## 1 3 12
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 177 9.41 7.82 7 8.75 8.9 0 30 30 0.58 -0.78 0.59
## Q0.25 Q0.75
## 1 3 16t.test(mult.pred.lm4.hd$hamd2 ~ mult.pred.lm4.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm4.hd$hamd2 by mult.pred.lm4.hd$match
## t = -1.6151, df = 322.71, p-value = 0.1073
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.8931484 0.2845058
## sample estimates:
## mean in group 0 mean in group 1
## 8.108108 9.412429cohen.d(mult.pred.lm4.hd$hamd2 ~ mult.pred.lm4.hd$match)##
## Cohen's d
##
## d estimate: -0.1775203 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.39707841 0.04203789#### Model 5 (Every IV)
for(z in 1:cv.num){
cat("running cv #", z, "\n") # Just a message thing
set.seed(2020+z)
cv = createFolds(cv.tx2, k = 5, list = T) # Creating folds for factual estimates in PAIs
num.el = sapply(cv, length)
cv_res = cbind(unlist(cv), rep(1:length(cv), num.el)) # Matrix with participants in one column and fold number in second
n = nrow(neo.depr.hd)
oos_predictions = matrix(NA, nrow = n, ncol = 3) # nX3 matrix
colnames(oos_predictions) = c("p_hat_cbt", "p_hat_med", "p_hat_diff")
for(cv_i in 1:length(cv)){ # For each 'k' fold in the CV procedure
cat("running fold #", cv_i, "\n")
cbt_index_nums = which(neo.depr.hd$txgrp==0)
# Getting participant numbers for those in training folds that also did CBT
cbt_indexnums_train = cbt_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], cbt_index_nums)]
cbt_indexnums_train = cbt_indexnums_train[!is.na(cbt_indexnums_train)]
cbt_indexnums_test = cbt_index_nums[match(cv_res[cv_res[,2]==cv_i,1], cbt_index_nums)]
cbt_indexnums_test = cbt_indexnums_test[!is.na(cbt_indexnums_test)]
med_index_nums = which(neo.depr.hd$txgrp==1)
med_indexnums_train = med_index_nums[match(cv_res[cv_res[,2]!=cv_i,1], med_index_nums)]
med_indexnums_train = med_indexnums_train[!is.na(med_indexnums_train)]
med_indexnums_test = med_index_nums[match(cv_res[cv_res[,2]==cv_i,1], med_index_nums)]
med_indexnums_test = med_indexnums_test[!is.na(med_indexnums_test)]
X_train_cbt_data = neo.depr.hd[cbt_indexnums_train,]
X_train_cbt_data = X_train_cbt_data[,c(1:32, 35, 36)]
X_train_cbt_data = rename(X_train_cbt_data, y = hamd2)
X_test_cbt = neo.depr.hd[cbt_indexnums_test,]
X_test_cbt = X_test_cbt[,c(1:32, 35, 36)]
X_test_cbt = rename(X_test_cbt, y = hamd2)
X_train_med_data = neo.depr.hd[med_indexnums_train,]
X_train_med_data = X_train_med_data[,c(1:32, 35, 36)]
X_train_med_data = rename(X_train_med_data, y = hamd2)
X_test_med = neo.depr.hd[med_indexnums_test,]
X_test_med = X_test_med[,c(1:32, 35, 36)]
X_test_med = rename(X_test_med, y = hamd2)
hd.cbt.train.model = regression.pai(data = X_train_cbt_data)
hd.med.train.model = regression.pai(data = X_train_med_data)
cbtpred_cbtgrp = predict(hd.cbt.train.model, X_test_cbt)
medpred_medgrp = predict(hd.med.train.model, X_test_med)
cbtpred_medgrp = predict(hd.cbt.lm5, X_test_med)
medpred_cbtgrp = predict(hd.med.lm5, X_test_cbt)
oos_predictions[cbt_indexnums_test, 1] = cbtpred_cbtgrp
oos_predictions[med_indexnums_test, 1] = cbtpred_medgrp
oos_predictions[med_indexnums_test, 2] = medpred_medgrp
oos_predictions[cbt_indexnums_test, 2] = medpred_cbtgrp
oos_predictions[cbt_indexnums_test, 3] = oos_predictions[cbt_indexnums_test, 1] - oos_predictions[cbt_indexnums_test, 2]
oos_predictions[med_indexnums_test, 3] = oos_predictions[med_indexnums_test,1] - oos_predictions[med_indexnums_test,2]
}
mult.pred.lm5.hd[, z] = oos_predictions[, 3]
}## running cv # 1
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 2
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 3
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 4
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 5
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 6
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 7
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 8
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 9
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5
## running cv # 10
## running fold # 1
## running fold # 2
## running fold # 3
## running fold # 4
## running fold # 5mult.pred.lm5.hd = as.data.frame(mult.pred.lm5.hd)
mult.pred.lm5.hd$mean = rowMeans(cbind(mult.pred.lm5.hd$V1, mult.pred.lm5.hd$V2, mult.pred.lm5.hd$V3, mult.pred.lm5.hd$V4, mult.pred.lm5.hd$V5,
mult.pred.lm5.hd$V6, mult.pred.lm5.hd$V7, mult.pred.lm5.hd$V8, mult.pred.lm5.hd$V9, mult.pred.lm5.hd$V10))
mult.pred.lm5.hd = cbind(mult.pred.lm5.hd, neo.depr.hd)
mult.pred.lm5.hd$indication = if_else(mult.pred.lm5.hd$mean < 0, 0, 1)
cbt.indicated.lm5.hd = filter(mult.pred.lm5.hd, indication == 0)
med.indicated.lm5.hd = filter(mult.pred.lm5.hd, indication == 1)
# Analyses across tx group
mult.pred.lm5.hd$match = if_else(mult.pred.lm5.hd$indication==mult.pred.lm5.hd$txgrp, 0, 1)
describeBy(mult.pred.lm5.hd$hamd2, group = mult.pred.lm5.hd$match, quant = c(.25, .75)) # Across txgrp##
## Descriptive statistics by group
## group: 0
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 139 7.79 6.26 7 7.22 5.93 0 26 26 0.72 -0.24 0.53
## Q0.25 Q0.75
## 1 3 12
## --------------------------------------------------------
## group: 1
## vars n mean sd median trimmed mad min max range skew kurtosis se
## 1 1 186 9.59 8.02 7.5 8.88 9.64 0 33 33 0.61 -0.66 0.59
## Q0.25 Q0.75
## 1 3 16t.test(mult.pred.lm5.hd$hamd2 ~ mult.pred.lm5.hd$match)##
## Welch Two Sample t-test
##
## data: mult.pred.lm5.hd$hamd2 by mult.pred.lm5.hd$match
## t = -2.2643, df = 322.36, p-value = 0.02422
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.3539753 -0.2353339
## sample estimates:
## mean in group 0 mean in group 1
## 7.791367 9.586022cohen.d(mult.pred.lm5.hd$hamd2 ~ mult.pred.lm5.hd$match)##
## Cohen's d
##
## d estimate: -0.2450938 (small)
## 95 percent confidence interval:
## lower upper
## -0.4664781 -0.0237095